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How to Write a Statistical Report
Last Updated: March 6, 2023 References Approved
This article was co-authored by wikiHow staff writer, Jennifer Mueller, JD . Jennifer Mueller is a wikiHow Content Creator. She specializes in reviewing, fact-checking, and evaluating wikiHow's content to ensure thoroughness and accuracy. Jennifer holds a JD from Indiana University Maurer School of Law in 2006. wikiHow marks an article as reader-approved once it receives enough positive feedback. In this case, several readers have written to tell us that this article was helpful to them, earning it our reader-approved status. This article has been viewed 372,544 times. Learn more...
A statistical report informs readers about about a particular subject or project. You can write a successful statistical report by formatting your report properly and including all the necessary information your readers need. [1] X Research source
Formatting Your Report
- If you're completing your report for a class, your instructor or professor may be willing to show you some reports submitted by previous students if you ask.
- University libraries also have copies of statistical reports created by students and faculty researchers on file. Ask the research librarian to help you locate one in your field of study.
- You also may be able to find statistical reports online that were created for business or marketing research, as well as those filed for government agencies.
- Be careful following samples exactly, particularly if they were completed for research in another field. Different fields of study have their own conventions regarding how a statistical report should look and what it should contain. For example, a statistical report by a mathematician may look incredibly different than one created by a market researcher for a retail business.
- You typically want to have 1-inch margins around all sides of your report. Be careful when adding visual elements such as charts and graphs to your report, and make sure they don't bleed over the margins or your report may not print properly and will look sloppy.
- You may want to have a 1.5-inch margin on the left-hand side of the page if you anticipate putting your study into a folder or binder, so all the words can be read comfortably when the pages are turned.
- Don't double-space your report unless you're writing it for a class assignment and the instructor or professor specifically tells you to do so.
- Use headers to add the page number to every page. You may also want to add your last name or the title of the study along with the page number.
- Citation methods typically are included in style manuals, which not only detail how you should cite your references but also have rules on acceptable punctuation and abbreviations, headings, and the general formatting of your report.
- For example, if you're writing a statistical report based on a psychological study, you typically must use the style manual published by the American Psychological Association (APA).
- Your citation method is all the more important if you anticipate your statistical report will be published in a particular trade or professional journal.
- If you're creating your statistical report for a class, a cover sheet may be required. Check with your instructor or professor or look on your assignment sheet to find out whether a cover sheet is required and what should be included on it.
- For longer statistical reports, you may also want to include a table of contents. You won't be able to format this until after you've finished the report, but it will list each section of your report and the page on which that section starts.
- If you decide to create section headings, they should be bold-faced and set off in such a way that they stand out from the rest of the text. For example, you may want to center bold-faced headings and use a slightly larger font size.
- Make sure a section heading doesn't fall at the bottom of the page. You should have at least a few lines of text, if not a full paragraph, below each section heading before the page break.
- Check the margins around visual elements and make sure the text lines up and is not too close to the visual element. You want it to be clear where the text ends and the words associated with the visual element (such as the axis labels for a graph) begin.
- Visual elements can cause your text to shift, so you'll need to double-check your section headings after your report is complete and make sure none of them are at the bottom of a page.
- Where possible, you also want to change your page breaks to eliminate situations in which the last line of a page is the first line of a paragraph, or the first line of a page is the last line of a paragraph. These are difficult to read.
Creating Your Content
- Avoid overly scientific or statistical language in your abstract as much as possible. Your abstract should be understandable to a larger audience than those who will be reading the entire report.
- It can help to think of your abstract as an elevator pitch. If you were in an elevator with someone and they asked you what your project was about, your abstract is what you would say to that person to describe your project.
- Even though your abstract appears first in your report, it's often easier to write it last, after you've completed the entire report.
- Aim for clear and concise language to set the tone for your report. Put your project in layperson's terms rather than using overly statistical language, regardless of the target audience of your report.
- If your report is based on a series of scientific experiments or data drawn from polls or demographic data, state your hypothesis or expectations going into the project.
- If other work has been done in the field regarding the same subject or similar questions, it's also appropriate to include a brief review of that work after your introduction. Explain why your work is different or what you hope to add to the existing body of work through your research.
- Include a description of any particular methods you used to track results, particularly if your experiments or studies were longer-term or observational in nature.
- If you had to make any adjustments during the development of the project, identify those adjustments and explain what required you to make them.
- List any software, resources, or other materials you used in the course of your research. If you used any textbook material, a reference is sufficient – there's no need to summarize that material in your report.
- Start with your main results, then include subsidiary results or interesting facts or trends you discovered.
- Generally you want to stay away from reporting results that have nothing to do with your original expectations or hypotheses. However, if you discovered something startling and unexpected through your research, you may want to at least mention it.
- This typically will be the longest section of your report, with the most detailed statistics. It also will be the driest and most difficult section for your readers to get through, especially if they are not statisticians.
- Small graphs or charts often show your results more clearly than you can write them in text.
- When you get to this section of your report, leave the heavy, statistical language behind. This section should be easy for anyone to understand, even if they skipped over your results section.
- If any additional research or study is necessary to further explore your hypotheses or answer questions that arose in the context of your project, describe that as well.
- It is often the case that you see things in hindsight that would have made data-gathering easier or more efficient. This is the place to discuss those. Since the scientific method is designed so that others can repeat your study, you want to pass on to future researchers your insights.
- Any speculation you have, or additional questions that came to mind over the course of your study, also are appropriate here. Just make sure you keep it to a minimum – you don't want your personal opinions and speculation to overtake the project itself.
- For example, if you compared your study to a similar study conducted in another city the year before yours, you would want to include a citation to that report in your references.
- Cite your references using the appropriate citation method for your discipline or field of study.
- Avoid citing any references that you did not mention in your report. For example, you may have done some background reading in preparation for your project. However, if you didn't end up directly citing any of those sources in your report, there's no need to list them in your references.
- Avoid trade "terms of art" or industry jargon if your report will be read mainly by people outside your particular industry.
- Make sure the terms of art and statistical terms that you do use in your report are used correctly. For example, you shouldn't use the word "average" in a statistical report because people often use that word to refer to different measures. Instead, use "mean," "median," or "mode" – whichever is correct.
Presenting Your Data
- This is particularly important if you're submitting your report for publication in a trade journal. If the pages are different sizes than the paper you print your report on, your visual elements won't line up the same way in the journal as they do in your manuscript.
- This also can be a factor if your report will be published online, since different display sizes can cause visual elements to display differently.
- The easiest way to label your visual elements is "Figure," followed by a number. Then you simply number each element sequentially in the order in which they appear in your report.
- Your title describes the information presented by the visual element. For example, if you've created a bar graph that shows the test scores of students on the chemistry class final, you might title it "Chemistry Final Test Scores, Fall 2016."
- Make sure each visual element is large enough in size that your readers can see everything they need to see without squinting. If you have to shrink down a graph to the point that readers can't make out the labels, it won't be very helpful to them.
- Create your visual elements using a format that you can easily import into your word-processing file. Importing using some graphics formats can distort the image or result in extremely low resolution.
- For example, if you have hundreds of samples, your x axis will be cluttered if you display each sample individually as a bar. However, you can move the measure on the y axis to the x axis, and use the y axis to measure the frequency.
- When your data include percentages, only go out to fractions of a percentage if your research demands it. If the smallest difference between your subjects is two percentage points, there's no need to display more than the whole percentage. However, if the difference between your subjects comes down to hundredths of a percent, you would need to display percentages to two decimal places so the graph would show the difference.
- For example, if your report includes a bar graph of the distribution of test scores for a chemistry class, and those scores are 97.56, 97.52, 97.46, and 97.61, your x axis would be each of the students and your y axis would start at 97 and go up to 98. This would highlight the differences in the students' scores.
- Be careful that your appendix does not overwhelm your report. You don't necessarily want to include every data sheet or other document you created over the course of your project.
- Rather, you only want to include documents that reasonably expand and lead to a further understanding of your report.
- For example, when describing your methods you state that a survey was conducted of students in a chemistry class to determine how they studied for the final exam. You might include a copy of the questions the students were asked in an appendix. However, you wouldn't necessarily need to include a copy of each student's answers to those questions.
Statistical Report Outline
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- ↑ https://www.ibm.com/docs/en/iotdm/11.3?topic=SSMLQ4_11.3.0/com.ibm.nex.optimd.dg.doc/11arcperf/oparcuse-r-statistical_reports.html
- ↑ https://www.examples.com/business/report/statistics-report.html
- ↑ https://collaboratory.ucr.edu/sites/g/files/rcwecm2761/files/2019-04/Final_Report_dan.pdf
- ↑ https://tex.stackexchange.com/questions/49386/what-is-the-recommended-font-to-use-for-a-statistical-table-in-an-academic-journ
- ↑ https://psychology.ucsd.edu/undergraduate-program/undergraduate-resources/academic-writing-resources/writing-research-papers/citing-references.html
- ↑ https://www.youtube.com/watch?v=kl3JOCmuil4
About This Article
Start your statistical report with an introduction explaining the purpose of your research. Then, dive into your research methods, how you collected data, and the experiments you conducted. Present you results with any necessary charts and graphs, but do not discuss or analyze the numbers -- in a statistical report, all analysis should happen in the conclusion. Once you’ve finished writing your report, draft a 200 word abstract and create a cover sheet with your name, the date, and the report title. Don’t forget to cite the appropriate references when necessary! For more formatting help, read on! Did this summary help you? Yes No
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Reporting Statistics in APA Style | Guidelines & Examples
Published on April 1, 2021 by Pritha Bhandari . Revised on November 28, 2022.
The APA Publication Manual is commonly used for reporting research results in the social and natural sciences. This article walks you through APA Style standards for reporting statistics in academic writing.
Statistical analysis involves gathering and testing quantitative data to make inferences about the world. A statistic is any number that describes a sample : it can be a proportion, a range , or a measurement, among other things.
When reporting statistics, use these formatting rules and suggestions from APA where relevant.
Table of contents
Numbers and measurements, decimal places and leading zeros, formatting mathematical formulas, formatting statistical terms, reporting means and standard deviations, reporting chi-square tests, reporting z tests and t tests, reporting analysis of variance (anovas), reporting correlations, reporting regressions, reporting confidence intervals, frequently asked questions about apa style statistics.
In general, APA advises using words for numbers under 10 and numerals for 10 and greater . However, always spell out a number that appears at the start of a sentence (or rephrase).
You should always use numerals for:
- Exact numbers before units of measurement or time
- Mathematical equations
- Percentages and percentiles
- Ratios, decimals, and uncommon fractions
- Scores and points on scales (e.g., 7-point scale)
- Exact amounts of money
Units of measurement and time
Report exact measurements using numerals, and use symbols or abbreviations for common units of measurement when they accompany exact measurements. Include a space between the number and the abbreviation.
When stating approximate figures, use words to express numbers under 10, and spell out the names of units of measurement.
- The ball weighed 7 kg.
- The ball weighed approximately seven kilograms.
Measurements should be reported in metric units. If you recorded measurements in non-metric units, include metric equivalents in your report as well as the original units.
Percentages
Use numerals for percentages along with the percent symbol (%). Don’t insert a space between the number and the symbol.
Words for “percent” or “percentage” should only be used in text when numbers aren’t used, or when a percentage appears at the start of a sentence.
- Of these respondents, 15% agreed with the statement.
- Fifteen percent of respondents agreed with the statement.
- The percentage was higher in 2020.
The number of decimal places to report depends on what you’re reporting. Generally, you should aim to round numbers while retaining precision. It’s best to present fewer decimal digits to aid easy understanding.
The following guidelines are usually applicable.
Use two or three decimal places and report exact values for all p values greater than .001. For p values smaller than .001, report them as p < .001.
Leading zeros
A leading zero is zero before the decimal point for numbers less than one. In APA Style, it’s only used in some cases.
Use a leading zero only when the statistic you’re describing can be greater than one. If it can never exceed one, omit the leading zero.
- Consumers reported high satisfaction with the services ( M = 4.1, SD = 0.8).
- The correlation was medium-sized ( r = .35).
- Although significant results were obtained, the effect was relatively small ( p = .015, d = 0.11).
Prevent plagiarism. Run a free check.
Provide formulas only when you use new or uncommon equations. For short equations, present them within one line in the main text whenever possible.
Make the order of operations as clear as possible by using parentheses (round brackets) for the first step, brackets [square brackets] for the second step, and braces {curly brackets} for the third step, where necessary.
More complex equations, or equations that take more than one line, should be displayed on their own lines. Equations should be displayed and numbered if you will reference them later on, regardless of their complexity. Number equations by placing the numbers in parentheses near the right edge of the page.
![\begin{equation*}\sqrt[3]{x}-3ac\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,\,(1)\end{equation*}](https://www.scribbr.com/wp-content/ql-cache/quicklatex.com-cee92bd73c0e2b85fc5f90e088a957b5_l3.png "Rendered by QuickLaTeX.com")
When reporting statistical results , present information in easily understandable ways. You can use a mix of text, tables, and figures to present data effectively when you have a lot of numbers to report.
In your main text, use helpful words like “respectively” or “in order” to aid understanding when listing several statistics in a sequence.
The APA manual provides guidelines for dealing with statistical terms, symbols and abbreviations.
Symbols and abbreviations
Population parameters are often represented with Greek letters, while sample statistics are often represented with italicized Latin letters.
Use the population symbol ( N ) for the total number of elements in a sample, and use the sample symbol ( n ) for the number of elements in each subgroup of the full sample.
In general, abbreviations should be defined on first use, but this isn’t always the case for common statistical abbreviations.
Capitalization, italicization and hyphenation
Statistical terms such as t test, z test, and p value always begin with a lowercase, italicized letter. Never begin a sentence with lowercase statistical abbreviations.
These statistical terms should only be hyphenated when they modify a subsequent word (e.g., “ z -test results” versus results of “ z tests”).
You can form plurals of statistical symbols (e.g., M or p ) by adding a non-italicized “s” to the end with no apostrophe (e.g., M s or p s).
In general, the following guidelines apply.
Parentheses vs. brackets
Always aim to avoid nested parentheses and brackets when reporting statistics. Instead, you should use commas to separate related statistics.
- Scores improved between the pretest and posttest ( p < .001).
- Significant differences in test scores were recorded, F (1, 30) = 4.67, p = .003.
- (A previous meta-analysis highlighted low effect sizes [ d = 0.1] in the field).
Report descriptive statistics to summarize your data. Quantitative data is often reported using means and standard deviations, while categorical data (e.g., demographic variables) is reported using proportions.
Means and standard deviations can be presented in the main text and/or in parentheses. You don’t need to repeat the units of measurement (e.g., centimeters) for statistics relating to the same data.
- Average sample height was 136.4 cm ( SD = 15.1).
- The height of the initial sample was relatively low ( M = 125.9 cm, SD = 16.6).
- Height significantly varied between children aged 5–7, 8–10, and 11–13. The means were 115.3, 133.5, and 149.1 cm, respectively.
To report the results of a chi-square test , include the following:
- the degrees of freedom ( df ) in parentheses
- the chi-square (Χ 2 ) value (also referred to as the chi-square test statistic)
- the p value
- A chi-square test of independence revealed a significant association between gender and product preference, Χ 2 (8) = 19.7, p = .012.
- Based on a chi-square test of goodness of fit , Χ 2 (4) = 11.34, p = .023, the sample’s distribution of religious affiliations matched that of the population’s.
For z tests
To report the results of a z test, include the following:
- the z value (also referred to as the z statistic or z score)
- The participants’ scores were higher than the population average, z = 2.48, p = .013.
- Higher scores were obtained on the new 20-item scale compared to the previous 40-item scale, z = 2.67, p = .007.
For t tests
To report the results of a t test , include the following:
- the t value (also referred to as the t statistic)
- Older adults experienced significantly more loneliness than younger adults, t (32) = 2.94, p = .006.
- Reaction times were significantly faster for mice in the experimental condition, t (53) = 5.94, p < .001.
To report the results of an ANOVA , include the following:
- the degrees of freedom (between groups, within groups) in parentheses
- the F value (also referred to as the F statistic)
- A one-way ANOVA demonstrated that the effect of leadership style was significant for employee engagement, F (2, 78) = 4.58, p = .013.
- We found a statistically significant main effect of age group on social media use, F (3, 117) = 3.19, p = .026.
To report the results of a correlation, include the following:
- the degrees of freedom in parentheses
- the r value (the correlation coefficient)
- We found a strong correlation between average temperature and new daily cases of COVID-19, r (357) = .42, p < .001.
Results of regression analyses are often displayed in a table because the output includes many numbers.
To report the results of a regression analysis in the text, include the following:
- the R 2 value (the coefficient of determination)
The format is usually:
- SAT scores predicted college GPA, R 2 = .34, F (1, 416) = 6.71, p = .009.
You should report confidence intervals of effect sizes (e.g., Cohen’s d ) or point estimates where relevant.
To report a confidence interval, state the confidence level and use brackets to enclose the lower and upper limits of the confidence interval, separated by a comma.
- Older adults experienced significantly more loneliness than younger adults, t (32) = 2.94, p = .006, d = 0.81, 95% CI [0.6, 1.02].
- On average, the treatment resulted in a 30% reduction in migraine frequency, 99% CI [26.5, 33.5].
When presenting multiple confidence intervals with the same confidence levels in a sequence, don’t repeat the confidence level or the word “CI.”
According to the APA guidelines, you should report enough detail on inferential statistics so that your readers understand your analyses.
Report the following for each hypothesis test:
- the test statistic value
- the degrees of freedom
- the exact p value (unless it is less than 0.001)
- the magnitude and direction of the effect
You should also present confidence intervals and estimates of effect sizes where relevant.
Use one decimal place for:
- Standard deviations
- Descriptive statistics based on discrete data
Use two decimal places for:
- Correlation coefficients
- Proportions
- Inferential test statistics such as t values, F values, and chi-squares.
In APA style, statistics can be presented in the main text or as tables or figures . To decide how to present numbers, you can follow APA guidelines:
- To present three or fewer numbers, try a sentence,
- To present between 4 and 20 numbers, try a table,
- To present more than 20 numbers, try a figure.
Since these are general guidelines, use your own judgment and feedback from others for effective presentation of numbers.
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Bhandari, P. (2022, November 28). Reporting Statistics in APA Style | Guidelines & Examples. Scribbr. Retrieved June 9, 2023, from https://www.scribbr.com/apa-style/numbers-and-statistics/
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Shouldn't the degrees of freedom follow the chi-square statistic without a space in between? And shouldn't the chi square statistic symbol be used instead of a 'normal' X?
Jack Caulfield (Scribbr Team)
You're right about the space; thanks for pointing it out, we'll update the article to remove the unnecessary spaces there.
As for your other question, the symbol you can see in the article is actually an uppercase chi: it looks essentially identical to an X in many fonts, but if you copy-paste it into Google you'll see that it's actually a chi!
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Purdue Online Writing Lab Purdue OWL® College of Liberal Arts
Writing with Descriptive Statistics
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This page is brought to you by the OWL at Purdue University. When printing this page, you must include the entire legal notice.
Copyright ©1995-2018 by The Writing Lab & The OWL at Purdue and Purdue University. All rights reserved. This material may not be published, reproduced, broadcast, rewritten, or redistributed without permission. Use of this site constitutes acceptance of our terms and conditions of fair use.
This handout explains how to write with statistics including quick tips, writing descriptive statistics, writing inferential statistics, and using visuals with statistics.
Usually there is no good way to write a statistic. It rarely sounds good, and often interrupts the structure or flow of your writing. Oftentimes the best way to write descriptive statistics is to be direct. If you are citing several statistics about the same topic, it may be best to include them all in the same paragraph or section.
The mean of exam two is 77.7. The median is 75, and the mode is 79. Exam two had a standard deviation of 11.6.
Overall the company had another excellent year. We shipped 14.3 tons of fertilizer for the year, and averaged 1.7 tons of fertilizer during the summer months. This is an increase over last year, where we shipped only 13.1 tons of fertilizer, and averaged only 1.4 tons during the summer months. (Standard deviations were as followed: this summer .3 tons, last summer .4 tons).
Some fields prefer to put means and standard deviations in parentheses like this:
If you have lots of statistics to report, you should strongly consider presenting them in tables or some other visual form. You would then highlight statistics of interest in your text, but would not report all of the statistics. See the section on statistics and visuals for more details.
If you have a data set that you are using (such as all the scores from an exam) it would be unusual to include all of the scores in a paper or article. One of the reasons to use statistics is to condense large amounts of information into more manageable chunks; presenting your entire data set defeats this purpose.
At the bare minimum, if you are presenting statistics on a data set, it should include the mean and probably the standard deviation. This is the minimum information needed to get an idea of what the distribution of your data set might look like. How much additional information you include is entirely up to you. In general, don't include information if it is irrelevant to your argument or purpose. If you include statistics that many of your readers would not understand, consider adding the statistics in a footnote or appendix that explains it in more detail.
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- Set the margin of your paper by 1.5 inch on the left side and 1 inch on the other sides.
- It should be single-spaced and has page numbers on the header.
- Font size must be 12 pt. and font style must either be Arial or Times New Roman.
- Cite your sources using a specific citation format that you wish to follow. Most of the time, researchers would use American Psychological Association (APA) format. You can also have Modern Language Association (MLA) format or the Chicago/Turabian style.
- Provide a cover page for your report. Write the name of the author and the date. If in case that you have a long report, you may include the table of contents for page reference.
- Do not forget to view your document before printing.
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How to Write a Report: A Guide
A report is a nonfiction account that presents and/or summarizes the facts about a particular event, topic, or issue. The idea is that people who are unfamiliar with the subject can find everything they need to know from a good report.
Reports make it easy to catch someone up to speed on a subject, but actually writing a report is anything but easy. So to help you understand what to do, below we present a little report of our own, all about report writing.
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What is a report?
In technical terms, the definition of a report is pretty vague: any account, spoken or written, of the matters concerning a particular topic. This could refer to anything from a courtroom testimony to a grade schooler’s book report.
Really, when people talk about “reports,” they’re usually referring to official documents outlining the facts of a topic, typically written by an expert on the subject or someone assigned to investigate it. There are different types of reports, explained in the next section, but they mostly fit this description.
What kind of information is shared in reports? Although all facts are welcome, reports, in particular, tend to feature these types of content:
- Details of an event or situation
- The consequences or ongoing effect of an event or situation
- Evaluation of statistical data or analytics
- Interpretations from the information in the report
- Predictions or recommendations based on the information in the report
- How the information relates to other events or reports
Reports are closely related to essay writing , although there are some clear distinctions. While both rely on facts, essays add the personal opinions and arguments of the authors. Reports typically stick only to the facts, although they may include some of the author’s interpretation of these facts, most likely in the conclusion.
Moreover, reports are heavily organized, commonly with tables of contents and copious headings and subheadings. This makes it easier for readers to scan reports for the information they’re looking for. Essays, on the other hand, are meant to be read start to finish, not browsed for specific insights.
Types of reports
There are a few different types of reports, depending on the purpose and to whom you present your report. Here’s a quick list of the common types of reports:
- Academic report: Tests a student’s comprehension of the subject matter, such as book reports, reports on historical events, and biographies
- Business reports: Identifies information useful in business strategy, such as marketing reports, internal memos, SWOT analysis, and feasibility reports
- Scientific reports: Shares research findings, such as research papers and case studies, typically in science journals
Reports can be further divided into categories based on how they are written. For example, a report could be formal or informal, short or long, and internal or external. In business, a vertical report shares information with people on different levels of the hierarchy (i.e., people who work above you and below you), while a lateral report is for people on the author’s same level, but in different departments.
There are as many types of reports as there are writing styles, but in this guide, we focus on academic reports, which tend to be formal and informational.
>>Read More: What Is Academic Writing?
What is the structure of a report?
The structure of a report depends on the type of report and the requirements of the assignment. While reports can use their own unique structure, most follow this basic template:
- Executive summary: Just like an abstract in an academic paper, an executive summary is a standalone section that summarizes the findings in your report so readers know what to expect. These are mostly for official reports and less so for school reports.
- Introduction: Setting up the body of the report, your introduction explains the overall topic that you’re about to discuss, with your thesis statement and any need-to-know background information before you get into your own findings.
- Body: The body of the report explains all your major discoveries, broken up into headings and subheadings. The body makes up the majority of the entire report; whereas the introduction and conclusion are just a few paragraphs each, the body can go on for pages.
- Conclusion: The conclusion is where you bring together all the information in your report and come to a definitive interpretation or judgment. This is usually where the author inputs their own personal opinions or inferences.
If you’re familiar with how to write a research paper , you’ll notice that report writing follows the same introduction-body-conclusion structure, sometimes adding an executive summary. Reports usually have their own additional requirements as well, such as title pages and tables of content, which we explain in the next section.
What should be included in a report?
There are no firm requirements for what’s included in a report. Every school, company, laboratory, task manager, and teacher can make their own format, depending on their unique needs. In general, though, be on the lookout for these particular requirements—they tend to crop up a lot:
- Title page: Official reports often use a title page to keep things organized; if a person has to read multiple reports, title pages make them easier to keep track of.
- Table of contents: Just like in books, the table of contents helps readers go directly to the section they’re interested in, allowing for faster browsing.
- Page numbering: A common courtesy if you’re writing a longer report, page numbering makes sure the pages are in order in the case of mix-ups or misprints.
- Headings and subheadings: Reports are typically broken up into sections, divided by headings and subheadings, to facilitate browsing and scanning.
- Citations: If you’re citing information from another source, the citations guidelines tell you the recommended format.
- Works cited page: A bibliography at the end of the report lists credits and the legal information for the other sources you got information from.
As always, refer to the assignment for the specific guidelines on each of these. The people who read the report should tell you which style guides or formatting they require.
How to write a report in 7 steps
Now let’s get into the specifics of how to write a report. Follow the seven steps on report writing below to take you from an idea to a completed paper.
1 Choose a topic based on the assignment
Before you start writing, you need to pick the topic of your report. Often, the topic is assigned for you, as with most business reports, or predetermined by the nature of your work, as with scientific reports. If that’s the case, you can ignore this step and move on.
If you’re in charge of choosing your own topic, as with a lot of academic reports, then this is one of the most important steps in the whole writing process. Try to pick a topic that fits these two criteria:
- There’s adequate information: Choose a topic that’s not too general but not too specific, with enough information to fill your report without padding, but not too much that you can’t cover everything.
- It’s something you’re interested in: Although this isn’t a strict requirement, it does help the quality of a report if you’re engaged by the subject matter.
Of course, don’t forget the instructions of the assignment, including length, so keep those in the back of your head when deciding.
2 Conduct research
With business and scientific reports, the research is usually your own or provided by the company—although there’s still plenty of digging for external sources in both.
For academic papers, you’re largely on your own for research, unless you’re required to use class materials. That’s one of the reasons why choosing the right topic is so crucial; you won’t go far if the topic you picked doesn’t have enough available research.
The key is to search only for reputable sources: official documents, other reports, research papers, case studies, books from respected authors, etc. Feel free to use research cited in other similar reports. You can often find a lot of information online through search engines, but a quick trip to the library can also help in a pinch.
3 Write a thesis statement
Before you go any further, write a thesis statement to help you conceptualize the main theme of your report. Just like the topic sentence of a paragraph, the thesis statement summarizes the main point of your writing, in this case, the report.
Once you’ve collected enough research, you should notice some trends and patterns in the information. If these patterns all infer or lead up to a bigger, overarching point, that’s your thesis statement.
For example, if you were writing a report on the wages of fast-food employees, your thesis might be something like, “Although wages used to be commensurate with living expenses, after years of stagnation they are no longer adequate.” From there, the rest of your report will elaborate on that thesis, with ample evidence and supporting arguments.
It’s good to include your thesis statement in both the executive summary and introduction of your report, but you still want to figure it out early so you know which direction to go when you work on your outline next.
4 Prepare an outline
Writing an outline is recommended for all kinds of writing, but it’s especially useful for reports given their emphasis on organization. Because reports are often separated by headings and subheadings, a solid outline makes sure you stay on track while writing without missing anything.
Really, you should start thinking about your outline during the research phase, when you start to notice patterns and trends. If you’re stuck, try making a list of all the key points, details, and evidence you want to mention. See if you can fit them into general and specific categories, which you can turn into headings and subheadings respectively.
5 Write a rough draft
Actually writing the rough draft , or first draft, is usually the most time-consuming step. Here’s where you take all the information from your research and put it into words. To avoid getting overwhelmed, simply follow your outline step by step to make sure you don’t accidentally leave out anything.
Don’t be afraid to make mistakes; that’s the number one rule for writing a rough draft. Expecting your first draft to be perfect adds a lot of pressure. Instead, write in a natural and relaxed way, and worry about the specific details like word choice and correcting mistakes later. That’s what the last two steps are for, anyway.
6 Revise and edit your report
Once your rough draft is finished, it’s time to go back and start fixing the mistakes you ignored the first time around. (Before you dive right back in, though, it helps to sleep on it to start editing fresh, or at least take a small break to unwind from writing the rough draft.)
We recommend first rereading your report for any major issues, such as cutting or moving around entire sentences and paragraphs. Sometimes you’ll find your data doesn’t line up, or that you misinterpreted a key piece of evidence. This is the right time to fix the “big picture” mistakes and rewrite any longer sections as needed.
If you’re unfamiliar with what to look for when editing, you can read our previous guide with some more advanced self-editing tips .
7 Proofread and check for mistakes
Last, it pays to go over your report one final time, just to optimize your wording and check for grammatical or spelling mistakes. In the previous step you checked for “big picture” mistakes, but here you’re looking for specific, even nitpicky problems.
A writing assistant like Grammarly flags those issues for you. Grammarly’s free version points out any spelling and grammatical mistakes while you write, with suggestions to improve your writing that you can apply with just one click. The Premium version offers even more advanced features, such as tone adjustments and word choice recommendations for taking your writing to the next level.
- How to Write a Statistical Report?
- How to Write a Statistical Report and Make This Process a Doddle
How to Write a Statistical Report: Prologue
The process of writing a statistical data analysis report example, how to do a statistical analysis report: the role of formatting, example of statistical analysis report mistakes: don’t be fooled, where can i get another good statistical analysis report example.
While Luxembourgian economy is relatively small with the total GDP estimating around $58 billion as of 2015, it is characterized by a very high level of incomes and living standards.
Effective communication is a key to success at any modern work environment. The opening phrase of the article is an example of the Statistics class homework writing assignment. Would you like to learn how to write a statistical report? It is important to develop adequate statistical skills supported by the knowledge of the subject, reading, research, and solid writing skills. The post includes valuable tips on how to do a statistical analysis report of a winner.
Statistics is a complex subject. Make your way through by purchasing cheap homework solutions online from the web’s top experienced academic writers! Say ' I need help with homework ' and get top-notch assistance. It is time to learn how to do a statistical analysis report.
Do not hurry up to understand how to write a statistics report – learn what the term means. Everyone knows Statistics is a complex academic discipline, which involves a plenty of calculations and interpretations of large data sets. It is a scientific term. Read more about statistics in different aspects of life here.
We will start explaining how to write a statistical report with the structure and organization of this type of academic/business assignment.
A 200-word abstract is a perfect way to start many academic papers. This piece of writing contains a summary of the entire text, highlighted major points, target audience, and goals of the project. From this point, move smoothly to the introduction with the clear explanation of why the writer has chosen the specific topic.
The body of such project is different from other academic assignments. The writer must list and describe the chosen research methods and findings based on the obtained data. Why can it be harder? It takes more time & efforts than several body paragraphs with the corresponding number of arguments along with the supportive evidence. The last stage is the same in any type of academic writing: a conclusion.
The examples of topics to let the students realize how to write a statistical report are given in the article:
- Norway is the most prosperous economies in Europe based on research
- A vaccine ABC to fight cancer effectively requires more time – data collected by one of the most influential American Healthcare Organizations
- The recent invention in the field of biotechnology is not effective
- Time-tested tools to fix problems with slow Windows – comparison of the outcomes with the analogical software running on Mac OS X
- The impact of depressions on the children aged 14-16
These papers require statistics and facts.
There is no way to understand how to do a statistical analysis report without recognizing the importance of correct formatting. Do it in the next section!
How to do a statistical analysis report regarding formatting? In most cases, the students face the formatting approaches listed below:
It all depends on the area of study. While Psychology teachers prefer assigning essays to be written in APA paper format , Sociology tutors recommend using ASA style . The students can read writing style manuals available online.
If the student were not involved in statistical report writing before, he/she would benefit from having a look at the valuable online examples of different similar papers to use as the good templates. Without this information, the student won’t learn how to write a statistical report properly. Ask the teacher about the format in case he forgets to five one.
In addition to online databases of papers, the students may attend school or college library, which contains at least one great example left from the previous educational years. Smart students share their works to support newer generations.
Mind that you have no right to copy the information from the selected example: plagiarism results in F grade.
The most recommended format is shown below.
Make single-inch margins around the sides of the page; watch out when attaching components like charts, tables, and graphs to the project. Leave a 1.5-inch margin on the left-hand side. Do it in case you plan to put the project into a folder/binder. Unlike other homework writing assignments (essay, research paper ), this type of task does not require a double-spaced format.
Do not forget about the headers! They should specify the number of pages, brief version of the title, author’s name, and some other details reminding the person what he/she is reading.
The last thing left to do to learn how to do a statistical analysis report is to analyze the most common mistakes Statistics students make.
We have discussed how to write a statistical analysis report of A-level; never forget to check the finished papers to detect possible mistakes. Those could be small, insignificant typos, which will not influence the final grade; those could be serious failures (grammar, word choice, etc.) Read the finished paper to find the following issues:
- Incomplete or incorrect punctuation
- Grammar errors
- Spelling mistakes
- Incorrect font
- Wrong paragraph/line breaks
- Missing words/phrases
- Incorrect amounts in tables & graphs
Is there nothing to fix? The absence of technical mistakes does not mean the author cannot find a better way to express certain things. Leave your final draft away. Come back to the essay in a few days to read it with a fresh look and rested the eye; fix any parts you believe sound ridiculous or can be improved.
Writing statistical reports is a time-consuming process, which requires in-depth knowledge of the studied subject, writing & research skills, ability to assume and analyze things competitively.
Congratulations – now you know how to do a statistical analysis report, it's goals, formatting, and the mistakes to avoid during the process. Need more information? To learn how to write a statistical report in more details, grab some good examples online. Students who are more interested in obtaining professional help instead of learning how to write a statistical report should remember about the opportunity to order a custom research paper from the professional essayists at our website!
School and college teachers often ask students to use the Harvard outline format (known as date-and-author referencing) to write their academic assignments. This type of your instructor’s request may shock you because you understand neither standard Harvard outline format rules or guidelines nor APA...
Nowadays scholars, professors and academics regard ASA (or American Sociological Association) format as the chief method of citation, along with APA or MLA styles. ASA citation format causes difficulties to students, in spite of the fact that there is nothing hard about it.This format, as well as AS...
The first sign of getting older in terms of education is a new writing assignment known as a research paper. Compared to a regular high school or college homework - writing an essay, college research paper requires much more time and effort. For example - you don’t have to add an abstract and appen...
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- How to Report Statistics
Ensure appropriateness and rigor, avoid flexibility and above all never manipulate results
In many fields, a statistical analysis forms the heart of both the methods and results sections of a manuscript. Learn how to report statistical analyses, and what other context is important for publication success and future reproducibility.
A matter of principle
First and foremost, the statistical methods employed in research must always be:
Appropriate for the study design
Rigorously reported in sufficient detail for others to reproduce the analysis
Free of manipulation, selective reporting, or other forms of “spin”
Just as importantly, statistical practices must never be manipulated or misused . Misrepresenting data, selectively reporting results or searching for patterns that can be presented as statistically significant, in an attempt to yield a conclusion that is believed to be more worthy of attention or publication is a serious ethical violation. Although it may seem harmless, using statistics to “spin” results can prevent publication, undermine a published study, or lead to investigation and retraction.
Supporting public trust in science through transparency and consistency
Along with clear methods and transparent study design, the appropriate use of statistical methods and analyses impacts editorial evaluation and readers’ understanding and trust in science.
In 2011 False-Positive Psychology: Undisclosed Flexibility in Data Collection and Analysis Allows Presenting Anything as Significant exposed that “flexibility in data collection, analysis, and reporting dramatically increases actual false-positive rates” and demonstrated “how unacceptably easy it is to accumulate (and report) statistically significant evidence for a false hypothesis”.
Arguably, such problems with flexible analysis lead to the “ reproducibility crisis ” that we read about today.
A constant principle of rigorous science The appropriate, rigorous, and transparent use of statistics is a constant principle of rigorous, transparent, and Open Science. Aim to be thorough, even if a particular journal doesn’t require the same level of detail. Trust in science is all of our responsibility. You cannot create any problems by exceeding a minimum standard of information and reporting.
Sound statistical practices
While it is hard to provide statistical guidelines that are relevant for all disciplines, types of research, and all analytical techniques, adherence to rigorous and appropriate principles remains key. Here are some ways to ensure your statistics are sound.
Define your analytical methodology before you begin Take the time to consider and develop a thorough study design that defines your line of inquiry, what you plan to do, what data you will collect, and how you will analyze it. (If you applied for research grants or ethical approval, you probably already have a plan in hand!) Refer back to your study design at key moments in the research process, and above all, stick to it.
To avoid flexibility and improve the odds of acceptance, preregister your study design with a journal Many journals offer the option to submit a study design for peer review before research begins through a practice known as preregistration. If the editors approve your study design, you’ll receive a provisional acceptance for a future research article reporting the results. Preregistering is a great way to head off any intentional or unintentional flexibility in analysis. By declaring your analytical approach in advance you’ll increase the credibility and reproducibility of your results and help address publication bias, too. Getting peer review feedback on your study design and analysis plan before it has begun (when you can still make changes!) makes your research even stronger AND increases your chances of publication—even if the results are negative or null. Never underestimate how much you can help increase the public’s trust in science by planning your research in this way.
Imagine replicating or extending your own work, years in the future Imagine that you are describing your approach to statistical analysis for your future self, in exactly the same way as we have described for writing your methods section . What would you need to know to replicate or extend your own work? When you consider that you might be at a different institution, working with different colleagues, using different programs, applications, resources — or maybe even adopting new statistical techniques that have emerged — you can help yourself imagine the level of reporting specificity that you yourself would require to redo or extend your work. Consider:
- Which details would you need to be reminded of?
- What did you do to the raw data before analysis?
- Did the purpose of the analysis change before or during the experiments?
- What participants did you decide to exclude?
- What process did you adjust, during your work?
Even if a necessary adjustment you made was not ideal, transparency is the key to ensuring this is not regarded as an issue in the future. It is far better to transparently convey any non-optimal techniques or constraints than to conceal them, which could result in reproducibility or ethical issues downstream.
Existing standards, checklists, guidelines for specific disciplines
You can apply the Open Science practices outlined above no matter what your area of expertise—but in many cases, you may still need more detailed guidance specific to your own field. Many disciplines, fields, and projects have worked hard to develop guidelines and resources to help with statistics, and to identify and avoid bad statistical practices. Below, you’ll find some of the key materials.
TIP: Do you have a specific journal in mind?
Be sure to read the submission guidelines for the specific journal you are submitting to, in order to discover any journal- or field-specific policies, initiatives or tools to utilize.
Articles on statistical methods and reporting
Makin, T.R., Orban de Xivry, J. Science Forum: Ten common statistical mistakes to watch out for when writing or reviewing a manuscript . eLife 2019;8:e48175 (2019). https://doi.org/10.7554/eLife.48175
Munafò, M., Nosek, B., Bishop, D. et al. A manifesto for reproducible science . Nat Hum Behav 1, 0021 (2017). https://doi.org/10.1038/s41562-016-0021
Writing tips
Your use of statistics should be rigorous, appropriate, and uncompromising in avoidance of analytical flexibility. While this is difficult, do not compromise on rigorous standards for credibility!
- Remember that trust in science is everyone’s responsibility.
- Keep in mind future replicability.
- Consider preregistering your analysis plan to have it (i) reviewed before results are collected to check problems before they occur and (ii) to avoid any analytical flexibility.
- Follow principles, but also checklists and field- and journal-specific guidelines.
- Consider a commitment to rigorous and transparent science a personal responsibility, and not simple adhering to journal guidelines.
- Be specific about all decisions made during the experiments that someone reproducing your work would need to know.
- Consider a course in advanced and new statistics, if you feel you have not focused on it enough during your research training.
Don’t
- Misuse statistics to influence significance or other interpretations of results
- Conduct your statistical analyses if you are unsure of what you are doing—seek feedback (e.g. via preregistration) from a statistical specialist first.
- How to Write a Great Title
- How to Write an Abstract
- How to Write Your Methods
- How to Write Discussions and Conclusions
- How to Edit Your Work
There’s a lot to consider when deciding where to submit your work. Learn how to choose a journal that will help your study reach its audience, while reflecting your values as a researcher…
The discussion section contains the results and outcomes of a study. An effective discussion informs readers what can be learned from your…
A thoughtful, thorough approach to your revision response now can save you time in further rounds of review. You’ve just spent months…
How to Write Data Analysis Reports in 9 Easy Steps
Imagine a bunch of bricks. They don’t have a purpose until you put them together into a house, do they?
In business intelligence, data is your building material, and a quality data analysis report is what you want to see as the result.
But if you’ve ever tried to use the collected data and assemble it into an insightful report, you know it’s not an easy job to do. Data is supposed to tell a story about your performance, but there’s a long way from unprocessed, raw data to a meaningful narrative that you can use to create an actionable plan for making steady progress towards your goals.
This article will help you improve the quality of your data analysis reports and build them effortlessly and fast. Let’s jump right in.
What Is a Data Analysis Report?
Why is data analysis reporting important, how to write a data analysis report 9 simple steps, data analysis report examples.
A data analysis report is a type of business report in which you present quantitative and qualitative data to evaluate your strategies and performance. Based on this data, you give recommendations for further steps and business decisions while using the data as evidence that backs up your evaluation.
Today, data analysis is one of the most important elements of business intelligence strategies as companies have realized the potential of having data-driven insights at hand to help them make data-driven decisions.
Just like you’ll look at your car’s dashboard if something’s wrong, you’ll pull your data to see what’s causing drops in website traffic, conversions, or sales – or any other business metric you may be following. This unprocessed data still doesn’t give you a diagnosis – it’s the first step towards a quality analysis. Once you’ve extracted and organized your data, it’s important to use graphs and charts to visualize it and make it easier to draw conclusions.
Once you add meaning to your data and create suggestions based on it, you have a data analysis report.
A vital detail everyone should know about data analysis reports is their accessibility for everyone in your team, and the ability to innovate. Your analysis report will contain your vital KPIs, so you can see where you’re reaching your targets and achieving goals, and where you need to speed up your activities or optimize your strategy. If you can uncover trends or patterns in your data, you can use it to innovate and stand out by offering even more valuable content, services, or products to your audience.
Data analysis is vital for companies for several reasons.
A reliable source of information
Trusting your intuition is fine, but relying on data is safer. When you can base your action plan on data that clearly shows that something is working or failing, you won’t only justify your decisions in front of the management, clients, or investors, but you’ll also be sure that you’ve taken appropriate steps to fix an issue or seize an important opportunity.
A better understanding of your business
According to Databox’s State of Business Reporting , most companies stated that regular monitoring and reporting improved progress monitoring, increased team effectiveness, allowed them to identify trends more easily, and improved financial performance. Data analysis makes it easier to understand your business as a whole, and each aspect individually. You can see how different departments analyze their workflow and how each step impacts their results in the end, by following their KPIs over time. Then, you can easily conclude what your business needs to grow – to boost your sales strategy, optimize your finances, or up your SEO game, for example.
An additional way to understand your business better is to compare your most important metrics and KPIs against companies that are just like yours. With Databox Benchmarks , you will need only one spot to see how all of your teams stack up against your peers and competitors.
Instantly and Anonymously Benchmark Your Company’s Performance Against Others Just Like You
If you ever asked yourself:
- How does our marketing stack up against our competitors?
- Are our salespeople as productive as reps from similar companies?
- Are our profit margins as high as our peers?
Databox Benchmark Groups can finally help you answer these questions and discover how your company measures up against similar companies based on your KPIs.
When you join Benchmark Groups, you will:
- Get instant, up-to-date data on how your company stacks up against similar companies based on the metrics most important to you. Explore benchmarks for dozens of metrics, built on anonymized data from thousands of companies and get a full 360° view of your company’s KPIs across sales, marketing, finance, and more.
- Understand where your business excels and where you may be falling behind so you can shift to what will make the biggest impact. Leverage industry insights to set more effective, competitive business strategies. Explore where exactly you have room for growth within your business based on objective market data.
- Keep your clients happy by using data to back up your expertise. Show your clients where you’re helping them overperform against similar companies. Use the data to show prospects where they really are… and the potential of where they could be.
- Get a valuable asset for improving yearly and quarterly planning . Get valuable insights into areas that need more work. Gain more context for strategic planning.
The best part?
- Benchmark Groups are free to access.
- The data is 100% anonymized. No other company will be able to see your performance, and you won’t be able to see the performance of individual companies either.
When it comes to showing you how your performance compares to others, here is what it might look like for the metric Average Session Duration:
And here is an example of an open group you could join:
And this is just a fraction of what you’ll get. With Databox Benchmarks, you will need only one spot to see how all of your teams stack up — marketing, sales, customer service, product development, finance, and more.
- Choose criteria so that the Benchmark is calculated using only companies like yours
- Narrow the benchmark sample using criteria that describe your company
- Display benchmarks right on your Databox dashboards
Sounds like something you want to try out? Join a Databox Benchmark Group today!
It makes data accessible to everyone
Data doesn’t represent a magical creature reserved for data scientists only anymore. Now that you have streamlined and easy-to-follow data visualizations and tools that automatically show the latest figures, you can include everyone in the decision-making process as they’ll understand what means what in the charts and tables. The data may be complex, but it becomes easy to read when combined with proper illustrations. And when your teams gain such useful and accessible insight, they will feel motivated to act on it immediately.
Better collaboration
Data analysis reports help teams collaborate better, as well. You can apply the SMART technique to your KPIs and goals, because your KPIs become assignable. When they’re easy to interpret for your whole team, you can assign each person with one or multiple KPIs that they’ll be in charge of. That means taking a lot off a team leader’s plate so they can focus more on making other improvements in the business. At the same time, removing inaccurate data from your day-to-day operations will improve friction between different departments, like marketing and sales, for instance.
More productivity
You can also expect increased productivity, since you’ll be saving time you’d otherwise spend on waiting for specialists to translate data for other departments, etc. This means your internal procedures will also be on a top level.
Want to give value with your data analysis report? It’s critical to master the skill of writing a quality data analytics report. Want to know how to report on data efficiently? We’ll share our secret in the following section.
Start with an Outline
Make a selection of vital kpis, pick the right charts for appealing design, use a narrative, organize the information, include a summary, careful with your recommendations, double-check everything, use interactive dashboards.
If you start writing without having a clear idea of what your data analysis report is going to include, it may get messy. Important insights may slip through your fingers, and you may stray away too far from the main topic. To avoid this, start the report by writing an outline first. Plan the structure and contents of each section first to make sure you’ve covered everything, and only then start crafting the report.
Don’t overwhelm the audience by including every single metric there is. You can discuss your whole dashboard in a meeting with your team, but if you’re creating data analytics reports for other departments or the executives, it’s best to focus on the most relevant KPIs that demonstrate the data important for the overall business performance.
PRO TIP: How Well Are Your Marketing KPIs Performing?
Like most marketers and marketing managers, you want to know how well your efforts are translating into results each month. How much traffic and new contact conversions do you get? How many new contacts do you get from organic sessions? How are your email campaigns performing? How well are your landing pages converting? You might have to scramble to put all of this together in a single report, but now you can have it all at your fingertips in a single Databox dashboard.
Our Marketing Overview Dashboard includes data from Google Analytics and HubSpot Marketing with key performance metrics like:
- Sessions . The number of sessions can tell you how many times people are returning to your website. Obviously, the higher the better.
- New Contacts from Sessions . How well is your campaign driving new contacts and customers?
- Marketing Performance KPIs . Tracking the number of MQLs, SQLs, New Contacts and similar will help you identify how your marketing efforts contribute to sales.
- Email Performance . Measure the success of your email campaigns from HubSpot. Keep an eye on your most important email marketing metrics such as number of sent emails, number of opened emails, open rate, email click-through rate, and more.
- Blog Posts and Landing Pages . How many people have viewed your blog recently? How well are your landing pages performing?
Now you can benefit from the experience of our Google Analytics and HubSpot Marketing experts, who have put together a plug-and-play Databox template that contains all the essential metrics for monitoring your leads. It’s simple to implement and start using as a standalone dashboard or in marketing reports, and best of all, it’s free!
You can easily set it up in just a few clicks – no coding required.
To set up the dashboard, follow these 3 simple steps:
Step 1: Get the template
Step 2: Connect your HubSpot and Google Analytics accounts with Databox.
Step 3: Watch your dashboard populate in seconds.
If you’re showing historical data – for instance, how you’ve performed now compared to last month – it’s best to use timelines or graphs. For other data, pie charts or tables may be more suitable. Make sure you use the right data visualization to display your data accurately and in an easy-to-understand manner.
Do you work on analytics and reporting ? Just exporting your data into a spreadsheet doesn’t qualify as either of them. The fact that you’re dealing with data may sound too technical, but actually, your report should tell a story about your performance. What happened on a specific day? Did your organic traffic increase or suddenly drop? Why? And more. There are a lot of questions to answer and you can put all the responses together in a coherent, understandable narrative.
Before you start writing or building your dashboard, choose how you’re going to organize your data. Are you going to talk about the most relevant and general ones first? It may be the best way to start the report – the best practices typically involve starting with more general information and then diving into details if necessary.
Some people in your audience won’t have the time to read the whole report, but they’ll want to know about your findings. Besides, a summary at the beginning of your data analytics report will help the reader get familiar with the topic and the goal of the report. And a quick note: although the summary should be placed at the beginning, you usually write it when you’re done with the report. When you have the whole picture, it’s easier to extract the key points that you’ll include in the summary.
Your communication skills may be critical in data analytics reports. Know that some of the results probably won’t be satisfactory, which means that someone’s strategy failed. Make sure you’re objective in your recommendations and that you’re not looking for someone to blame. Don’t criticize, but give suggestions on how things can be improved. Being solution-oriented is much more important and helpful for the business.
The whole point of using data analytics tools and data, in general, is to achieve as much accuracy as possible. Avoid manual mistakes by proofreading your report when you finish, and if possible, give it to another person so they can confirm everything’s in place.
Using the right tools is just as important as the contents of your data analysis. The way you present it can make or break a good report, regardless of how valuable the data is. That said, choose a great reporting tool that can automatically update your data and display it in a visually appealing manner. Make sure it offers a streamlined dashboard that you can also customize depending on the purpose of the report.
To wrap up the guide, we decided to share nine excellent examples of what awesome data analysis reports can look like. You’ll learn what metrics you should include and how to organize them in logical sections to make your report beautiful and effective.
- Marketing Data Analysis Report Example
SEO Data Analysis Report Example
Sales data analysis report example.
- Customer Support Data Analysis Report Example
Help Desk Data Analysis Report Example
Ecommerce data analysis report example, project management data analysis report example, social media data analysis report example, financial kpi data analysis report example, marketing data report example.
If you need an intuitive dashboard that allows you to track your website performance effortlessly and monitor all the relevant metrics such as website sessions, pageviews, or CTA engagement, you’ll love this free HubSpot Marketing Website Overview dashboard template .
Tracking the performance of your SEO efforts is important. You can easily monitor relevant SEO KPIs like clicks by device, goal completions, or sessions by channel by downloading this SEO Campaign Performance dashboard template .
How successful is your sales team? It’s easy to analyze their performance and predict future growth if you choose this HubSpot CRM Sales Analytics Overview dashboard template and track metrics such as average time to close the deal, new deals amount, or average revenue per new client.
Customer Support Analysis Data Report Example
Customer support is one of the essential factors that impact your business growth. You can use this streamlined, customizable Customer Success dashboard template . In a single dashboard, you can monitor metrics such as customer satisfaction score, new MRR, or time to first response time.
Other than being free and intuitive, this HelpScout for Customer Support dashboard template is also customizable and enables you to track the most vital metrics that indicate your customer support agents’ performance: handle time, happiness score, interactions per resolution, and more.
Is your online store improving or failing? You can easily collect relevant data about your funnel and monitor the most important metrics like average order value, revenue by channel, or ecommerce conversion rate by downloading this Ecommerce Full Funnel dashboard template .
Does your IT department need feedback on their project management performance? Download this Jira dashboard template to track vital metrics such as issues created or resolved, issues by status, etc. Jira enables you to gain valuable insights into your teams’ productivity.
Need to know if your social media strategy is successful? You can find that out by using this easy-to-understand Social Media Awareness & Engagement dashboard template . Here you can monitor and analyze metrics like sessions by social source, track the number of likes and followers, and measure the traffic from each source.
Tracking your finances is critical for keeping your business profitable. If you want to monitor metrics such as the number of open invoices, open deals amount by stage by pipeline, or closed-won deals, use this free QuickBooks + HubSpot CRM Financial Performance dashboard template .
Rely on Accurate Data with Databox
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How to Write Descriptive Statistics in Dissertation & Examples
When it comes to data analysis in dissertations, descriptive statistics are a helpful tool for understanding and summarizing your results. In a dissertation, this can be helpful for orienting readers to the key findings of your study.
For instance, you might use descriptive statistics to give an overview of the demographic characteristics of your sample or the response rates to your survey.
Additionally, descriptive statistics can be used to highlight any significant relationships that you have found between variables.
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For example, you might use a table or graph to show the relationship between income and levels of satisfaction with life.
Let us now define descriptive statistics in relation to dissertation writing.
What is descriptive statistics in a dissertation?
Descriptive statistics are used in a dissertation to summarize data and describe patterns that are observed in the data. This type of statistics can be used to describe the distribution of a variable, the relationship between two variables, or to compare groups of observations.
For example, if you were interested in studying the relationship between income and education level, you could use descriptive statistics to examine the average income for each education level. This would give you a general idea of the relationship between these two variables. However, keep in mind that descriptive statistics do not allow you to make any conclusions about cause and effect .
In other words, just because there is a relationship between two variables does not mean that one variable is causing the other. If you want to make causal inferences, you will need to use inferential statistics.
How to use descriptive statistics when writing a dissertation
When writing a dissertation, it is important to make use of descriptive statistics in order to effectively analyze your data. Descriptive statistics can help you to summarize your data and identify trends. However, it is important to use them correctly in order to avoid making inaccurate conclusions. Here are some tips for using descriptive statistics when writing a dissertation:
First, make sure that you understand the different types of descriptive statistics and when to use each one. For example, mean, median, and mode are all measures of central tendency, but each one is used in different situations.
Second, always report both the numeric values and graphical representations of your data. This will help readers to better understand your results.
Finally, make sure that you interpret your results correctly. Remember that correlation does not imply causation! Just because two variables are correlated does not mean that one causes the other.
By following these tips, you can ensure that you make effective use of descriptive statistics in your dissertation.
Measures of central tendency
The mean is simply the average of all the values in your data set. It is calculated by adding up all the values and then dividing by the number of values. The mean is a very useful measure of central tendency, but it is important to remember that it can be affected by outliers. Outliers are values that are far away from the rest of the data. For example, if you have a data set with values 1, 2, 3, 4, and 100, then 100 would be an outlier. Outliers can often skew the mean, so it is important to be aware of them when interpreting your results.
The mode is the least used measure of central tendency in dissertation writing, yet it has its own unique advantages. The mode is the value that appears most frequently in a data set. To calculate the mode, simply count how often each value appears and choose the value that appears most often. The mode is easy to calculate by hand, which can be helpful when working with small data sets. The mode is also resistant to outliers, which means it is not as affected by extreme values as the mean or median. However, the mode has some limitations. It can be difficult to calculate when data are not equally distributed, and it can be influenced by small changes in the data set. In general, the mode is a useful tool for descriptive statistics, but it should be used with caution.
The median is the measure of central tendency in descriptive statistics that is placed exactly in the middle of a sorted data list. It is also the number that has an equal probability of being selected above or below it. In a data set, the median assigns a score to each data point that indicates its relationship to all other members of the set. The median can be used to determine the most typical value in a data set, and it is especially useful when there are outliers present. The median is less affected by outliers than the mean, making it a more reliable measure of central tendency. For these reasons, the median is often used in dissertations as a measure of central tendency.
Measures of variability
One of the first measures of variability that is used in descriptive statistics is the range. The range tells you the difference between the highest and lowest values in a set of data. It is a simple concept, but it can be very useful in understanding how spread out your data is. The range is affected by outliers, which are values that are far away from the rest of the data. For example, if you have a set of data that ranges from 1 to 10, but you also have one outlier value that is 100, then the range will be 99. This means that the outlier value has a big impact on the range.
Interquartile range
The interquartile range is a measure of variability that is commonly used in descriptive statistics. It is calculated by taking the difference between the upper and lower quartiles. The interquartile range is a useful measure of variability because it is not affected by outliers, and it is easy to calculate. The interquartile range can be used to compare data sets, or to compare different groups of data within a data set. For example, the interquartile range can be used to compare the variability of two groups of students, or to compare the variability of two courses. The interquartile range can also be used to compare the variability of two populations, or to compare the variability of two samples.
Variance and standard deviation
The variance is simply the average of the squared differences from the mean. The standard deviation is simply the square root of the variance. These measures are important because they give you a sense of how spread out the data is. If the data is very spread out, then it is more likely to be affected by outliers. On the other hand, if the data is clustered around the mean, then it is less likely to be affected by outliers. Therefore, measures of variability can help you to identify potential problems with your data.
Normal Distribution
The Normal Distribution is the most important distribution in statistics because it is so frequently used to model data. It is also known as the bell-shaped curve because of its characteristic shape. The Normal Distribution is defined by its mean and standard deviation. The mean is the center of the distribution, and the standard deviation is a measure of how spread out the data are. The Normal Distribution is used to describe many real-world phenomena, such as height, weight, IQ scores, and test scores. In addition, the Normal Distribution is used to model random processes, such as the roll of a dice or the flip of a coin.
Normal Distribution is a very powerful tool for understanding data. However, it is important to remember that the Normal Distribution does not always provide an accurate description of data.
For example, if data are skewed or have outliers, then the Normal Distribution may not be appropriate. Nonetheless, the Normal Distribution is a valuable tool that should be used when analyzing data.
Confidence Interval
Another measure of variability in descriptive statistics is the confidence interval. This interval tells us how confident we can be that a population mean falls within a certain range.
The confidence interval is calculated using the sample mean, the standard deviation of the sample, and the size of the sample.
The larger the sample size, the more confident we can be that the population mean falls within the confidence interval. The confidence interval is a important measure of variability because it allows us to make inferences about a population based on a sample.
Descriptive statistics are a crucial tool for any researcher, and this is especially true when writing a dissertation. There are many different ways to use descriptive statistics, but some of the most common include calculating means, medians, and standard deviations. These measures can help to give an overview of your data, and can be used to compare groups or to look for trends over time.
Descriptive statistics can also be used to create visuals, such as graphs and charts. When used effectively, descriptive statistics can make your data more understandable and convincing.
If you’re not sure how to get started, there are many great resources available that can walk you through the process step-by-step. With a little practice, you’ll be using descriptive statistics like a pro in no time.
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- http://orcid.org/0000-0003-4574-1761 Alok Kumar Dwivedi
- Department of Molecular and Translational Medicine, Division of Biostatistics and Epidemiology , Texas Tech University Health Sciences Center El Paso , El Paso , Texas , USA
- Correspondence to Dr Alok Kumar Dwivedi, Department of Molecular and Translational Medicine. Division of Biostatistics & Epidemiology., Texas Tech University Health Sciences Center El Paso, El Paso, Texas, USA; alok.dwivedi{at}ttuhsc.edu
Reporting of statistical analysis is essential in any clinical and translational research study. However, medical research studies sometimes report statistical analysis that is either inappropriate or insufficient to attest to the accuracy and validity of findings and conclusions. Published works involving inaccurate statistical analyses and insufficient reporting influence the conduct of future scientific studies, including meta-analyses and medical decisions. Although the biostatistical practice has been improved over the years due to the involvement of statistical reviewers and collaborators in research studies, there remain areas of improvement for transparent reporting of the statistical analysis section in a study. Evidence-based biostatistics practice throughout the research is useful for generating reliable data and translating meaningful data to meaningful interpretation and decisions in medical research. Most existing research reporting guidelines do not provide guidance for reporting methods in the statistical analysis section that helps in evaluating the quality of findings and data interpretation. In this report, we highlight the global and critical steps to be reported in the statistical analysis of grants and research articles. We provide clarity and the importance of understanding study objective types, data generation process, effect size use, evidence-based biostatistical methods use, and development of statistical models through several thematic frameworks. We also provide published examples of adherence or non-adherence to methodological standards related to each step in the statistical analysis and their implications. We believe the suggestions provided in this report can have far-reaching implications for education and strengthening the quality of statistical reporting and biostatistical practice in medical research.
- Biostatistics
- Biomedical Research
- Education, Medical
Data availability statement
Data sharing not applicable as no datasets generated and/or analyzed for this study.
This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, an indication of whether changes were made, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/ .
http://dx.doi.org/10.1136/jim-2022-002479
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Introduction
Biostatistics is the overall approach to how we realistically and feasibly execute a research idea to produce meaningful data and translate data to meaningful interpretation and decisions. In this era of evidence-based medicine and practice, basic biostatistical knowledge becomes essential for critically appraising research articles and implementing findings for better patient management, improving healthcare, and research planning. 1 However, it may not be sufficient for the proper execution and reporting of statistical analyses in studies. 2 3 Three things are required for statistical analyses, namely knowledge of the conceptual framework of variables, research design, and evidence-based applications of statistical analysis with statistical software. 4 5 The conceptual framework provides possible biological and clinical pathways between independent variables and outcomes with role specification of variables. The research design provides a protocol of study design and data generation process (DGP), whereas the evidence-based statistical analysis approach provides guidance for selecting and implementing approaches after evaluating data with the research design. 2 5 Ocaña-Riola 6 reported a substantial percentage of articles from high-impact medical journals contained errors in statistical analysis or data interpretation. These errors in statistical analyses and interpretation of results do not only impact the reliability of research findings but also influence the medical decision-making and planning and execution of other related studies. A survey of consulting biostatisticians in the USA reported that researchers frequently request biostatisticians for performing inappropriate statistical analyses and inappropriate reporting of data. 7 This implies that there is a need to enforce standardized reporting of the statistical analysis section in medical research which can also help rreviewers and investigators to improve the methodological standards of the study.
Biostatistical practice in medicine has been improving over the years due to continuous efforts in promoting awareness and involving expert services on biostatistics, epidemiology, and research design in clinical and translational research. 8–11 Despite these efforts, the quality of reporting of statistical analysis in research studies has often been suboptimal. 12 13 We noticed that none of the methods reporting documents were developed using evidence-based biostatistics (EBB) theory and practice. The EBB practice implies that the selection of statistical analysis methods for statistical analyses and the steps of results reporting and interpretation should be grounded based on the evidence generated in the scientific literature and according to the study objective type and design. 5 Previous works have not properly elucidated the importance of understanding EBB concepts and related reporting in the write-up of statistical analyses. As a result, reviewers sometimes ask to present data or execute analyses that do not match the study objective type. 14 We summarize the statistical analysis steps to be reported in the statistical analysis section based on review and thematic frameworks.
We identified articles describing statistical reporting problems in medicine using different search terms ( online supplemental table 1 ). Based on these studies, we prioritized commonly reported statistical errors in analytical strategies and developed essential components to be reported in the statistical analysis section of research grants and studies. We also clarified the purpose and the overall implication of reporting each step in statistical analyses through various examples.
Supplemental material
Although biostatistical inputs are critical for the entire research study ( online supplemental table 2 ), biostatistical consultations were mostly used for statistical analyses only 15 . Even though the conduct of statistical analysis mismatched with the study objective and DGP was identified as the major problem in articles submitted to high-impact medical journals. 16 In addition, multivariable analyses were often inappropriately conducted and reported in published studies. 17 18 In light of these statistical errors, we describe the reporting of the following components in the statistical analysis section of the study.
Step 1: specify study objective type and outcomes (overall approach)
The study objective type provides the role of important variables for a specified outcome in statistical analyses and the overall approach of the model building and model reporting steps in a study. In the statistical framework, the problems are classified into descriptive and inferential/analytical/confirmatory objectives. In the epidemiological framework, the analytical and prognostic problems are broadly classified into association, explanatory, and predictive objectives. 19 These study objectives ( figure 1 ) may be classified into six categories: (1) exploratory, (2) association, (3) causal, (4) intervention, (5) prediction and (6) clinical decision models in medical research. 20
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Comparative assessments of developing and reporting of study objective types and models. Association measures include odds ratio, risk ratio, or hazard ratio. AUC, area under the curve; C, confounder; CI, confidence interval; E, exposure; HbA1C: hemoglobin A1c; M, mediator; MFT, model fit test; MST, model specification test; PI, predictive interval; R 2 , coefficient of determinant; X, independent variable; Y, outcome.
The exploratory objective type is a specific type of determinant study and is commonly known as risk factors or correlates study in medical research. In an exploratory study, all covariates are considered equally important for the outcome of interest in the study. The goal of the exploratory study is to present the results of a model which gives higher accuracy after satisfying all model-related assumptions. In the association study, the investigator identifies predefined exposures of interest for the outcome, and variables other than exposures are also important for the interpretation and considered as covariates. The goal of an association study is to present the adjusted association of exposure with outcome. 20 In the causal objective study, the investigator is interested in determining the impact of exposure(s) on outcome using the conceptual framework. In this study objective, all variables should have a predefined role (exposures, confounders, mediators, covariates, and predictors) in a conceptual framework. A study with a causal objective is known as an explanatory or a confirmatory study in medical research. The goal is to present the direct or indirect effects of exposure(s) on an outcome after assessing the model’s fitness in the conceptual framework. 19 21 The objective of an interventional study is to determine the effect of an intervention on outcomes and is often known as randomized or non-randomized clinical trials in medical research. In the intervention objective model, all variables other than the intervention are treated as nuisance variables for primary analyses. The goal is to present the direct effect of the intervention on the outcomes by eliminating biases. 22–24 In the predictive study, the goal is to determine an optimum set of variables that can predict the outcome, particularly in external settings. The clinical decision models are a special case of prognostic models in which high dimensional data at various levels are used for risk stratification, classification, and prediction. In this model, all variables are considered input features. The goal is to present a decision tool that has high accuracy in training, testing, and validation data sets. 20 25 Biostatisticians or applied researchers should properly discuss the intention of the study objective type before proceeding with statistical analyses. In addition, it would be a good idea to prepare a conceptual model framework regardless of study objective type to understand study concepts.
A study 26 showed a favorable effect of the beta-blocker intervention on survival outcome in patients with advanced human epidermal growth factor receptor (HER2)-negative breast cancer without adjusting for all the potential confounding effects (age or menopausal status and Eastern Cooperative Oncology Performance Status) in primary analyses or validation analyses or using a propensity score-adjusted analysis, which is an EBB preferred method for analyzing non-randomized studies. 27 Similarly, another study had the goal of developing a predictive model for prediction of Alzheimer’s disease progression. 28 However, this study did not internally or externally validate the performance of the model as per the requirement of a predictive objective study. In another study, 29 investigators were interested in determining an association between metabolic syndrome and hepatitis C virus. However, the authors did not clearly specify the outcome in the analysis and produced conflicting associations with different analyses. 30 Thus, the outcome should be clearly specified as per the study objective type.
Step 2: specify effect size measure according to study design (interpretation and practical value)
The study design provides information on the selection of study participants and the process of data collection conditioned on either exposure or outcome ( figure 2 ). The appropriate use of effect size measure, tabular presentation of results, and the level of evidence are mostly determined by the study design. 31 32 In cohort or clinical trial study designs, the participants are selected based on exposure status and are followed up for the development of the outcome. These study designs can provide multiple outcomes, produce incidence or incidence density, and are preferred to be analyzed with risk ratio (RR) or hazards models. In a case–control study, the selection of participants is conditioned on outcome status. This type of study can have only one outcome and is preferred to be analyzed with an odds ratio (OR) model. In a cross-sectional study design, there is no selection restriction on outcomes or exposures. All data are collected simultaneously and can be analyzed with a prevalence ratio model, which is mathematically equivalent to the RR model. 33 The reporting of effect size measure also depends on the study objective type. For example, predictive models typically require reporting of regression coefficients or weight of variables in the model instead of association measures, which are required in other objective types. There are agreements and disagreements between OR and RR measures. Due to the constancy and symmetricity properties of OR, some researchers prefer to use OR in studies with common events. Similarly, the collapsibility and interpretability properties of RR make it more appealing to use in studies with common events. 34 To avoid variable practice and interpretation issues with OR, it is recommended to use RR models in all studies except for case–control and nested case–control studies, where OR approximates RR and thus OR models should be used. Otherwise, investigators may report sufficient data to compute any ratio measure. Biostatisticians should educate investigators on the proper interpretation of ratio measures in the light of study design and their reporting. 34 35
Effect size according to study design.
Investigators sometimes either inappropriately label their study design 36 37 or report effect size measures not aligned with the study design, 38 39 leading to difficulty in results interpretation and evaluation of the level of evidence. The proper labeling of study design and the appropriate use of effect size measure have substantial implications for results interpretation, including the conduct of systematic review and meta-analysis. 40 A study 31 reviewed the frequency of reporting OR instead of RR in cohort studies and randomized clinical trials (RCTs) and found that one-third of the cohort studies used an OR model, whereas 5% of RCTs used an OR model. The majority of estimated ORs from these studies had a 20% or higher deviation from the corresponding RR.
Step 3: specify study hypothesis, reporting of p values, and interval estimates (interpretation and decision)
The clinical hypothesis provides information for evaluating formal claims specified in the study objectives, while the statistical hypothesis provides information about the population parameters/statistics being used to test the formal claims. The inference about the study hypothesis is typically measured by p value and confidence interval (CI). A smaller p value indicates that the data support against the null hypothesis. Since the p value is a conditional probability, it can never tell about the acceptance or rejection of the null hypothesis. Therefore, multiple alternative strategies of p values have been proposed to strengthen the credibility of conclusions. 41 42 Adaption of these alternative strategies is only needed in the explanatory objective studies. Although exact p values are recommended to be reported in research studies, p values do not provide any information about the effect size. Compared with p values, the CI provides a confidence range of the effect size that contains the true effect size if the study were repeated and can be used to determine whether the results are statistically significant or not. 43 Both p value and 95% CI provide complementary information and thus need to be specified in the statistical analysis section. 24 44
Researchers often test one or more comparisons or hypotheses. Accordingly, the side and the level of significance for considering results to be statistically significant may change. Furthermore, studies may include more than one primary outcome that requires an adjustment in the level of significance for multiplicity. All studies should provide the interval estimate of the effect size/regression coefficient in the primary analyses. Since the interpretation of data analysis depends on the study hypothesis, researchers are required to specify the level of significance along with the side (one-sided or two-sided) of the p value in the test for considering statistically significant results, adjustment of the level of significance due to multiple comparisons or multiplicity, and reporting of interval estimates of the effect size in the statistical analysis section. 45
A study 46 showed a significant effect of fluoxetine on relapse rates in obsessive-compulsive disorder based on a one-sided p value of 0.04. Clearly, there was no reason for using a one-sided p value as opposed to a two-sided p value. A review of the appropriate use of multiple test correction methods in multiarm clinical trials published in major medical journals in 2012 identified over 50% of the articles did not perform multiple-testing correction. 47 Similar to controlling a familywise error rate due to multiple comparisons, adjustment of the false discovery rate is also critical in studies involving multiple related outcomes. A review of RCTs for depression between 2007 and 2008 from six journals reported that only limited studies (5.8%) accounted for multiplicity in the analyses due to multiple outcomes. 48
Step 4: account for DGP in the statistical analysis (accuracy)
The study design also requires the specification of the selection of participants and outcome measurement processes in different design settings. We referred to this specific design feature as DGP. Understanding DGP helps in determining appropriate modeling of outcome distribution in statistical analyses and setting up model premises and units of analysis. 4 DGP ( figure 3 ) involves information on data generation and data measures, including the number of measurements after random selection, complex selection, consecutive selection, pragmatic selection, or systematic selection. Specifically, DGP depends on a sampling setting (participants are selected using survey sampling methods and one subject may represent multiple participants in the population), clustered setting (participants are clustered through a recruitment setting or hierarchical setting or multiple hospitals), pragmatic setting (participants are selected through mixed approaches), or systematic review setting (participants are selected from published studies). DGP also depends on the measurements of outcomes in an unpaired setting (measured on one occasion only in independent groups), paired setting (measured on more than one occasion or participants are matched on certain subject characteristics), or mixed setting (measured on more than one occasion but interested in comparing independent groups). It also involves information regarding outcomes or exposure generation processes using quantitative or categorical variables, quantitative values using labs or validated instruments, and self-reported or administered tests yielding a variety of data distributions, including individual distribution, mixed-type distribution, mixed distributions, and latent distributions. Due to different DGPs, study data may include messy or missing data, incomplete/partial measurements, time-varying measurements, surrogate measures, latent measures, imbalances, unknown confounders, instrument variables, correlated responses, various levels of clustering, qualitative data, or mixed data outcomes, competing events, individual and higher-level variables, etc. The performance of statistical analysis, appropriate estimation of standard errors of estimates and subsequently computation of p values, the generalizability of findings, and the graphical display of data rely on DGP. Accounting for DGP in the analyses requires proper communication between investigators and biostatisticians about each aspect of participant selection and data collection, including measurements, occasions of measurements, and instruments used in the research study.
Common features of the data generation process.
A study 49 compared the intake of fresh fruit and komatsuna juice with the intake of commercial vegetable juice on metabolic parameters in middle-aged men using an RCT. The study was criticized for many reasons, but primarily for incorrect statistical methods not aligned with the study DGP. 50 Similarly, another study 51 highlighted that 80% of published studies using the Korean National Health and Nutrition Examination Survey did not incorporate survey sampling structure in statistical analyses, producing biased estimates and inappropriate findings. Likewise, another study 52 highlighted the need for maintaining methodological standards while analyzing data from the National Inpatient Sample. A systematic review 53 identified that over 50% of studies did not specify whether a paired t-test or an unpaired t-test was performed in statistical analysis in the top 25% of physiology journals, indicating poor transparency in reporting of statistical analysis as per the data type. Another study 54 also highlighted the data displaying errors not aligned with DGP. As per DGP, delay in treatment initiation of patients with cancer defined from the onset of symptom to treatment initiation should be analyzed into three components: patient/primary delay, secondary delay, and tertiary delay. 55 Similarly, the number of cancerous nodes should be analyzed with count data models. 56 However, several studies did not analyze such data according to DGP. 57 58
Step 5: apply EBB methods specific to study design features and DGP (efficiency and robustness)
The continuous growth in the development of robust statistical methods for dealing with a specific problem produced various methods to analyze specific data types. Since multiple methods are available for handling a specific problem yet with varying performances, heterogeneous practices among applied researchers have been noticed. Variable practices could also be due to a lack of consensus on statistical methods in literature, unawareness, and the unavailability of standardized statistical guidelines. 2 5 59 However, it becomes sometimes difficult to differentiate whether a specific method was used due to its robustness, lack of awareness, lack of accessibility of statistical software to apply an alternative appropriate method, intention to produce expected results, or ignorance of model diagnostics. To avoid heterogeneous practices, the selection of statistical methodology and their reporting at each stage of data analysis should be conducted using methods according to EBB practice. 5 Since it is hard for applied researchers to optimally select statistical methodology at each step, we encourage investigators to involve biostatisticians at the very early stage in basic, clinical, population, translational, and database research. We also appeal to biostatisticians to develop guidelines, checklists, and educational tools to promote the concept of EBB. As an effort, we developed the statistical analysis and methods in biomedical research (SAMBR) guidelines for applied researchers to use EBB methods for data analysis. 5 The EBB practice is essential for applying recent cutting-edge robust methodologies to yield accurate and unbiased results. The efficiency of statistical methodologies depends on the assumptions and DGP. Therefore, investigators may attempt to specify the choice of specific models in the primary analysis as per the EBB.
Although details of evidence-based preferred methods are provided in the SAMBR checklists for each study design/objective, 5 we have presented a simplified version of evidence-based preferred methods for common statistical analysis ( online supplemental table 3 ). Several examples are available in the literature where inefficient methods not according to EBB practice have been used. 31 57 60
Step 6: report variable selection method in the multivariable analysis according to study objective type (unbiased)
Multivariable analysis can be used for association, prediction or classification or risk stratification, adjustment, propensity score development, and effect size estimation. 61 Some biological, clinical, behavioral, and environmental factors may directly associate or influence the relationship between exposure and outcome. Therefore, almost all health studies require multivariable analyses for accurate and unbiased interpretations of findings ( figure 1 ). Analysts should develop an adjusted model if the sample size permits. It is a misconception that the analysis of RCT does not require adjusted analysis. Analysis of RCT may require adjustment for prognostic variables. 23 The foremost step in model building is the entry of variables after finalizing the appropriate parametric or non-parametric regression model. In the exploratory model building process due to no preference of exposures, a backward automated approach after including any variables that are significant at 25% in the unadjusted analysis can be used for variable selection. 62 63 In the association model, a manual selection of covariates based on the relevance of the variables should be included in a fully adjusted model. 63 In a causal model, clinically guided methods should be used for variable selection and their adjustments. 20 In a non-randomized interventional model, efforts should be made to eliminate confounding effects through propensity score methods and the final propensity score-adjusted multivariable model may adjust any prognostic variables, while a randomized study simply should adjust any prognostic variables. 27 Maintaining the event per variable (EVR) is important to avoid overfitting in any type of modeling; therefore, screening of variables may be required in some association and explanatory studies, which may be accomplished using a backward stepwise method that needs to be clarified in the statistical analyses. 10 In a predictive study, a model with an optimum set of variables producing the highest accuracy should be used. The optimum set of variables may be screened with the random forest method or bootstrap or machine learning methods. 64 65 Different methods of variable selection and adjustments may lead to different results. The screening process of variables and their adjustments in the final multivariable model should be clearly mentioned in the statistical analysis section.
A study 66 evaluating the effect of hydroxychloroquine (HDQ) showed unfavorable events (intubation or death) in patients who received HDQ compared with those who did not (hazard ratio (HR): 2.37, 95% CI 1.84 to 3.02) in an unadjusted analysis. However, the propensity score-adjusted analyses as appropriate with the interventional objective model showed no significant association between HDQ use and unfavorable events (HR: 1.04, 95% CI 0.82 to 1.32), which was also confirmed in multivariable and other propensity score-adjusted analyses. This study clearly suggests that results interpretation should be based on a multivariable analysis only in observational studies if feasible. A recent study 10 noted that approximately 6% of multivariable analyses based on either logistic or Cox regression used an inappropriate selection method of variables in medical research. This practice was more commonly noted in studies that did not involve an expert biostatistician. Another review 61 of 316 articles from high-impact Chinese medical journals revealed that 30.7% of articles did not report the selection of variables in multivariable models. Indeed, this inappropriate practice could have been identified more commonly if classified according to the study objective type. 18 In RCTs, it is uncommon to report an adjusted analysis based on prognostic variables, even though an adjusted analysis may produce an efficient estimate compared with an unadjusted analysis. A study assessing the effect of preemptive intervention on development outcomes showed a significant effect of an intervention on reducing autism spectrum disorder symptoms. 67 However, this study was criticized by Ware 68 for not reporting non-significant results in unadjusted analyses. If possible, unadjusted estimates should also be reported in any study, particularly in RCTs. 23 68
Step 7: provide evidence for exploring effect modifiers (applicability)
Any variable that modifies the effect of exposure on the outcome is called an effect modifier or modifier or an interacting variable. Exploring the effect modifiers in multivariable analyses helps in (1) determining the applicability/generalizability of findings in the overall or specific subpopulation, (2) generating ideas for new hypotheses, (3) explaining uninterpretable findings between unadjusted and adjusted analyses, (4) guiding to present combined or separate models for each specific subpopulation, and (5) explaining heterogeneity in treatment effect. Often, investigators present adjusted stratified results according to the presence or absence of an effect modifier. If the exposure interacts with multiple variables statistically or conceptually in the model, then the stratified findings (subgroup) according to each effect modifier may be presented. Otherwise, stratified analysis substantially reduces the power of the study due to the lower sample size in each stratum and may produce significant results by inflating type I error. 69 Therefore, a multivariable analysis involving an interaction term as opposed to a stratified analysis may be presented in the presence of an effect modifier. 70 Sometimes, a quantitative variable may emerge as a potential effect modifier for exposure and an outcome relationship. In such a situation, the quantitative variable should not be categorized unless a clinically meaningful threshold is not available in the study. In fact, the practice of categorizing quantitative variables should be avoided in the analysis unless a clinically meaningful cut-off is available or a hypothesis requires for it. 71 In an exploratory objective type, any possible interaction may be obtained in a study; however, the interpretation should be guided based on clinical implications. Similarly, some objective models may have more than one exposure or intervention and the association of each exposure according to the level of other exposure should be presented through adjusted analyses as suggested in the presence of interaction effects. 70
A review of 428 articles from MEDLINE on the quality of reporting from statistical analyses of three (linear, logistic, and Cox) commonly used regression models reported that only 18.5% of the published articles provided interaction analyses, 17 even though interaction analyses can provide a lot of useful information.
Step 8: assessment of assumptions, specifically the distribution of outcome, linearity, multicollinearity, sparsity, and overfitting (reliability)
The assessment and reporting of model diagnostics are important in assessing the efficiency, validity, and usefulness of the model. Model diagnostics include satisfying model-specific assumptions and the assessment of sparsity, linearity, distribution of outcome, multicollinearity, and overfitting. 61 72 Model-specific assumptions such as normal residuals, heteroscedasticity and independence of errors in linear regression, proportionality in Cox regression, proportionality odds assumption in ordinal logistic regression, and distribution fit in other types of continuous and count models are required. In addition, sparsity should also be examined prior to selecting an appropriate model. Sparsity indicates many zero observations in the data set. 73 In the presence of sparsity, the effect size is difficult to interpret. Except for machine learning models, most of the parametric and semiparametric models require a linear relationship between independent variables and a functional form of an outcome. Linearity should be assessed using a multivariable polynomial in all model objectives. 62 Similarly, the appropriate choice of the distribution of outcome is required for model building in all study objective models. Multicollinearity assessment is also useful in all objective models. Assessment of EVR in multivariable analysis can be used to avoid the overfitting issue of a multivariable model. 18
Some review studies highlighted that 73.8%–92% of the articles published in MEDLINE had not assessed the model diagnostics of the multivariable regression models. 17 61 72 Contrary to the monotonically, linearly increasing relationship between systolic blood pressure (SBP) and mortality established using the Framingham’s study, 74 Port et al 75 reported a non-linear relationship between SBP and all-cause mortality or cardiovascular deaths by reanalysis of the Framingham’s study data set. This study identified a different threshold for treating hypertension, indicating the role of linearity assessment in multivariable models. Although a non-Gaussian distribution model may be required for modeling patient delay outcome data in cancer, 55 a study analyzed patient delay data using an ordinary linear regression model. 57 An investigation of the development of predictive models and their reporting in medical journals identified that 53% of the articles had fewer EVR than the recommended EVR, indicating over half of the published articles may have an overfitting model. 18 Another study 76 attempted to identify the anthropometric variables associated with non-insulin-dependent diabetes and found that none of the anthropometric variables were significant after adjusting for waist circumference, age, and sex, indicating the presence of collinearity. A study reported detailed sparse data problems in published studies and potential solutions. 73
Step 9: report type of primary and sensitivity analyses (consistency)
Numerous considerations and assumptions are made throughout the research processes that require assessment, evaluation, and validation. Some assumptions, executions, and errors made at the beginning of the study data collection may not be fixable 13 ; however, additional information collected during the study and data processing, including data distribution obtained at the end of the study, may facilitate additional considerations that need to be verified in the statistical analyses. Consistencies in the research findings via modifications in the outcome or exposure definition, study population, accounting for missing data, model-related assumptions, variables and their forms, and accounting for adherence to protocol in the models can be evaluated and reported in research studies using sensitivity analyses. 77 The purpose and type of supporting analyses need to be specified clearly in the statistical analyses to differentiate the main findings from the supporting findings. Sensitivity analyses are different from secondary or interim or subgroup analyses. 78 Data analyses for secondary outcomes are often referred to as secondary analyses, while data analyses of an ongoing study are called interim analyses and data analyses according to groups based on patient characteristics are known as subgroup analyses.
Almost all studies require some form of sensitivity analysis to validate the findings under different conditions. However, it is often underutilized in medical journals. Only 18%–20.3% of studies reported some forms of sensitivity analyses. 77 78 A review of nutritional trials from high-quality journals reflected that 17% of the conclusions were reported inappropriately using findings from sensitivity analyses not based on the primary/main analyses. 77
Step 10: provide methods for summarizing, displaying, and interpreting data (transparency and usability)
Data presentation includes data summary, data display, and data from statistical model analyses. The primary purpose of the data summary is to understand the distribution of outcome status and other characteristics in the total sample and by primary exposure status or outcome status. Column-wise data presentation should be preferred according to exposure status in all study designs, while row-wise data presentation for the outcome should be preferred in all study designs except for a case–control study. 24 32 Summary statistics should be used to provide maximum information on data distribution aligned with DGP and variable type. The purpose of results presentation primarily from regression analyses or statistical models is to convey results interpretation and implications of findings. The results should be presented according to the study objective type. Accordingly, the reporting of unadjusted and adjusted associations of each factor with the outcome may be preferred in the determinant objective model, while unadjusted and adjusted effects of primary exposure on the outcome may be preferred in the explanatory objective model. In prognostic models, the final predictive models may be presented in such a way that users can use models to predict an outcome. In the exploratory objective model, a final multivariable model should be reported with R 2 or area under the curve (AUC). In the association and interventional models, the assessment of internal validation is critically important through various sensitivity and validation analyses. A model with better fit indices (in terms of R 2 or AUC, Akaike information criterion, Bayesian information criterion, fit index, root mean square error) should be finalized and reported in the causal model objective study. In the predictive objective type, the model performance in terms of R 2 or AUC in training and validation data sets needs to be reported ( figure 1 ). 20 21 There are multiple purposes of data display, including data distribution using bar diagram or histogram or frequency polygons or box plots, comparisons using cluster bar diagram or scatter dot plot or stacked bar diagram or Kaplan-Meier plot, correlation or model assessment using scatter plot or scatter matrix, clustering or pattern using heatmap or line plots, the effect of predictors with fitted models using marginsplot, and comparative evaluation of effect sizes from regression models using forest plot. Although the key purpose of data display is to highlight critical issues or findings in the study, data display should essentially follow DGP and variable types and should be user-friendly. 54 79 Data interpretation heavily relies on the effect size measure along with study design and specified hypotheses. Sometimes, variables require standardization for descriptive comparison of effect sizes among exposures or interpreting small effect size, or centralization for interpreting intercept or avoiding collinearity due to interaction terms, or transformation for achieving model-related assumptions. 80 Appropriate methods of data reporting and interpretation aligned with study design, study hypothesis, and effect size measure should be specified in the statistical analysis section of research studies.
Published articles from reputed journals inappropriately summarized a categorized variable with mean and range, 81 summarized a highly skewed variable with mean and standard deviation, 57 and treated a categorized variable as a continuous variable in regression analyses. 82 Similarly, numerous examples from published studies reporting inappropriate graphical display or inappropriate interpretation of data not aligned with DGP or variable types are illustrated in a book published by Bland and Peacock. 83 84 A study used qualitative data on MRI but inappropriately presented with a Box-Whisker plot. 81 Another study reported unusually high OR for an association between high breast parenchymal enhancement and breast cancer in both premenopausal and postmenopausal women. 85 This reporting makes suspicious findings and may include sparse data bias. 86 A poor tabular presentation without proper scaling or standardization of a variable, missing CI for some variables, missing unit and sample size, and inconsistent reporting of decimal places could be easily noticed in table 4 of a published study. 29 Some published predictive models 87 do not report intercept or baseline survival estimates to use their predictive models in clinical use. Although a direct comparison of effect sizes obtained from the same model may be avoided if the units are different among variables, 35 a study had an objective to compare effect sizes across variables but the authors performed comparisons without standardization of variables or using statistical tests. 88
A sample for writing statistical analysis section in medical journals/research studies
Our primary study objective type was to develop a (select from figure 1 ) model to assess the relationship of risk factors (list critical variables or exposures) with outcomes (specify type from continuous/discrete/count/binary/polytomous/time-to-event). To address this objective, we conducted a (select from figure 2 or any other) study design to test the hypotheses of (equality or superiority or non-inferiority or equivalence or futility) or develop prediction. Accordingly, the other variables were adjusted or considered as (specify role of variables from confounders, covariates, or predictors or independent variables) as reflected in the conceptual framework. In the unadjusted or preliminary analyses as per the (select from figure 3 or any other design features) DGP, (specify EBB preferred tests from online supplemental table 3 or any other appropriate tests) were used for (specify variables and types) in unadjusted analyses. According to the EBB practice for the outcome (specify type) and DGP of (select from figure 3 or any other), we used (select from online supplemental table 1 or specify a multivariable approach) as the primary model in the multivariable analysis. We used (select from figure 1 ) variable selection method in the multivariable analysis and explored the interaction effects between (specify variables). The model diagnostics including (list all applicable, including model-related assumptions, linearity, or multicollinearity or overfitting or distribution of outcome or sparsity) were also assessed using (specify appropriate methods) respectively. In such exploration, we identified (specify diagnostic issues if any) and therefore the multivariable models were developed using (specify potential methods used to handle diagnostic issues). The other outcomes were analyzed with (list names of multivariable approaches with respective outcomes). All the models used the same procedure (or specify from figure 1 ) for variable selection, exploration of interaction effects, and model diagnostics using (specify statistical approaches) depending on the statistical models. As per the study design, hypothesis, and multivariable analysis, the results were summarized with effect size (select as appropriate or from figure 2 ) along with (specify 95% CI or other interval estimates) and considered statistically significant using (specify the side of p value or alternatives) at (specify the level of significance) due to (provide reasons for choosing a significance level). We presented unadjusted and/or adjusted estimates of primary outcome according to (list primary exposures or variables). Additional analyses were conducted for (specific reasons from step 9) using (specify methods) to validate findings obtained in the primary analyses. The data were summarized with (list summary measures and appropriate graphs from step 10), whereas the final multivariable model performance was summarized with (fit indices if applicable from step 10). We also used (list graphs) as appropriate with DGP (specify from figure 3 ) to present the critical findings or highlight (specify data issues) using (list graphs/methods) in the study. The exposures or variables were used in (specify the form of the variables) and therefore the effect or association of (list exposures or variables) on outcome should be interpreted in terms of changes in (specify interpretation unit) exposures/variables. List all other additional analyses if performed (with full details of all models in a supplementary file along with statistical codes if possible).
Concluding remarks
We highlighted 10 essential steps to be reported in the statistical analysis section of any analytical study ( figure 4 ). Adherence to minimum reporting of the steps specified in this report may enforce investigators to understand concepts and approach biostatisticians timely to apply these concepts in their study to improve the overall quality of methodological standards in grant proposals and research studies. The order of reporting information in statistical analyses specified in this report is not mandatory; however, clear reporting of analytical steps applicable to the specific study type should be mentioned somewhere in the manuscript. Since the entire approach of statistical analyses is dependent on the study objective type and EBB practice, proper execution and reporting of statistical models can be taught to the next generation of statisticians by the study objective type in statistical education courses. In fact, some disciplines ( figure 5 ) are strictly aligned with specific study objective types. Bioinformaticians are oriented in studying determinant and prognostic models toward precision medicine, while epidemiologists are oriented in studying association and causal models, particularly in population-based observational and pragmatic settings. Data scientists are heavily involved in prediction and classification models in personalized medicine. A common thing across disciplines is using biostatistical principles and computation tools to address any research question. Sometimes, one discipline expert does the part of others. 89 We strongly recommend using a team science approach that includes an epidemiologist, biostatistician, data scientist, and bioinformatician depending on the study objectives and needs. Clear reporting of data analyses as per the study objective type should be encouraged among all researchers to minimize heterogeneous practices and improve scientific quality and outcomes. In addition, we also encourage investigators to strictly follow transparent reporting and quality assessment guidelines according to the study design ( https://www.equator-network.org/ ) to improve the overall quality of the study, accordingly STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) for observational studies, CONSORT (Consolidated Standards of Reporting Trials) for clinical trials, STARD (Standards for Reporting Diagnostic Accuracy Studies) for diagnostic studies, TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis OR Diagnosis) for prediction modeling, and ARRIVE (Animal Research: Reporting of In Vivo Experiments) for preclinical studies. The steps provided in this document for writing the statistical analysis section is essentially different from other guidance documents, including SAMBR. 5 SAMBR provides a guidance document for selecting evidence-based preferred methods of statistical analysis according to different study designs, while this report suggests the global reporting of essential information in the statistical analysis section according to study objective type. In this guidance report, our suggestion strictly pertains to the reporting of methods in the statistical analysis section and their implications on the interpretation of results. Our document does not provide guidance on the reporting of sample size or results or statistical analysis section for meta-analysis. The examples and reviews reported in this study may be used to emphasize the concepts and related implications in medical research.
Summary of reporting steps, purpose, and evaluation measures in the statistical analysis section.
Role of interrelated disciplines according to study objective type.
Ethics statements
Patient consent for publication.
Not required.
Acknowledgments
The author would like to thank the reviewers for their careful review and insightful suggestions.
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Supplementary material
Supplementary data.
This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.
- Data supplement 1
Contributors AKD developed the concept and design and wrote the manuscript.
Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.
Competing interests AKD is a Journal of Investigative Medicine Editorial Board member. No other competing interests declared.
Provenance and peer review Commissioned; externally peer reviewed.
Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.
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A Statistical Report: How to Write It Easily?
Communicative skills have a crucial role in modern society, and there are plenty of areas where you need to master them. One of such areas is a statistical report. In can be useful in both educational and professional purposes. How to write it properly? The answer to this question will be complex. To develop your statistical report writing skills you need to have a decent level of subject insight, reading, and research as well as writing skills, of course. In our article, you will learn some useful recommendations on how to become a master of statistical analysis report writing.
You also have an opportunity to skip the whole learning process by ordering your homework done by our academic writers who are experts in any possible academic field to help you. But we will share some writing tips with you anyway.
Before any special writing task, you need to make sure you understand the term correctly. It is a well-known truth that Statistics is a very complex subject that requires skills in calculations and data interpretation. You need to know how to explain the numbers you receive during calculations. This is real science. And there are plenty of areas where statistics allows a deeper understanding of the things.
We will discuss the process of writing statistical reports starting with its structure and organization of this academic or business writing task.
Examples of How to Write a Statistical Data Analysis Report
The best way to start your paper is to write an abstract with 200 words. This part of the paper will include the basic info the whole paper, pointing out main points, goals and the target readers. Then you should move gradually to the introductory part where you need to explain why you have chosen this particular topic.
The structure of the body will have some unique features comparing to other writing assignments. In this part, you should specify what research methods you have chosen and why as well as mention what finding did you get after analyzing the received data. This can be challenging as you need to more efforts and time to arrange the paragraphs and arguments along the text. Then you need to provide a logical conclusion.
Here are some examples of topics that can be used for writing statistical reports:
- Researches that prove that Norway is on the top place in Europe considering prosperity.
- We need more time to develop the anti-cancer vaccine – data, provided by one of the respectable Healthcare Organization in the USA.
- The newest biotechnological invention is not effective.
- What tools allow fixing the problem of the slow Windows performance in comparison with similar tools for Mac OS X.
- The effect of depression on kids 13-17 years old.
To write such papers, you will need to use facts and statistical data.
One more thing you need to realize after the statistical report term is the importance of the proper formatting. So the next chapter will be about this aspect.
The Importance of Formatting of a Statistical Analysis Report
Let’s take a closer look at the formatting issue. Usually, students have a list of the following formatting styles:
The choice usually depends on the study area. For instance, the teachers of Psychology require using APA format while writing an essay on their subject, while Sociology teachers prefer ASA. All the manuals on the required styles can be easily found online.
In case you did not write a statistical report before, we advise you to find some good examples of various papers of this type and use them as samples to develop your own paper eventually. In fact, students have to use various templates and examples to learn how to create a decent paper, especially when it comes to the statistical report writing. Just pay attention to the format you need and the one you should use for your paper.
Apart from online sources of information, you can get benefits from the college or university libraries. As a rule, they store numerous papers on various topics and styles that were approved in particular educational institutions. This is the way the best students provide help to the next generations.
But you need to realize that those examples are only for educational purposes and you cannot simply copy the data from them, because it will be considered as plagiarism.
Below you can find out what format is used more often and what its specifications are.
Make one-inch spaces from each side of the page. From the left side leave 1.5-inch space in case your paper will be placed into the folder with other works. Check the sizes of attached charts, tables and graphs as well. You do not have to use double-spacing in this type of academic paper though.
Pay attention to the header of each page. Include to it the number of the page, short version of the title, the name of the writer and other details that will reflect the material the person reads.
As soon as we now know how to format your paper correctly, let’s take a look at some typical mistakes that students make while working on their statistical report projects.
Typical Mistakes You Should Avoid While working on Your Statistical Report
You know how to make you a statistical report paper worth the best grade. But the most important thing is to check the paper when it is done. This will help you to detect some mistakes that could spoil the impression of the paper and fix them when you still can. Remember, even the minor issues could have a serious impact on the general “reputation” of your paper. Check your report on the following issues:
- punctuation mistakes;
- improper grammar and spelling;
- wrong font/paragraph marks/lines/etc.;
- missing words or phrases;
- a wrong number of attachments.
Does your paper contain any of these mistakes? Even if you know for sure that your paper is technically correct, you still need to revise it in order to check whether you have used the proper approaches and provide all the required explanations. As soon as you get the final draft, leave it for a few days if you have time and get back to it later. This method will help you to take a fresh look at your work.
Remember, that you are working on the writing task that is of the academic level, so you need to use all your skills and knowledge and improve them if needed to provide the best result.
Sources of the Examples of the Statistical Analysis Reports
After reading this article properly, you have a general understanding of what your report should look like considering content and the formatting. You can enhance your insight into academic writing by using available online sources of examples and guides. You also can save time and order the original statistical report on your topic. This may help you to master your skills in your next writing assignments.
The beauty market in 2023: A special State of Fashion report
The State of Fashion: Beauty
In 2022, the beauty market—defined as skincare, fragrance, makeup, and haircare—generated approximately $430 billion in revenue. Today, beauty is on an upward trajectory across all categories. It has proven to be resilient amid global economic crises and in a turbulent macroeconomic environment. Beauty is now an industry that many people, from top-tier financiers to A-list celebrities, want to be a part of—and with good reason. Following a solid recovery since the height of the COVID-19 pandemic, the beauty market is expected to reach approximately $580 billion by 2027, growing by a projected 6 percent a year (Exhibit 1). This is in line with or slightly higher than other consumer segments such as apparel, footwear, eyewear, pet care, and food and beverages.
About the authors
This article is a collaborative effort by Imran Amed, representing views from the Business of Fashion, and Achim Berg , Sara Hudson , Kristi Klitsch Weaver , and Megan Lesko Pacchia, representing views from McKinsey’s Consumer Packaged Goods and Retail Practices.
A dynamic segment that is ripe for disruption, the beauty industry will have reshaped itself around an expanding array of products, channels, and markets before this decade is over. Consumers, particularly younger generations, will spur this shift, as their own definitions of beauty morph while their perceptions of everything—from the meaning of sustainability and the role of influencers and key opinion leaders to the importance of self-care—evolve. Overall, beauty is expected to be characterized by “premiumization,” with the premium beauty tier projected to grow at an annual rate of 8 percent (compared with 5 percent in mass beauty) between 2022 and 2027, as consumers trade up and increase their spending, especially in fragrance and makeup.
At the same time, we expect the landscape to become even more competitive, as a range of independent brands that successfully came to market over the past decade seek to scale and as new challengers emerge. Intensifying competition will prompt incumbent brands and retailers to change as well. In line with the trend-driven dynamics in the market, 42 percent of respondents to McKinsey’s 2023 survey of consumers across China, France, Germany, Italy, the United Kingdom, and the United States say they enjoy trying new brands. Meanwhile, consumers are increasingly shopping across price points and report that both online and offline stores influence their shopping behavior. Their preference for omnichannel shopping is expected to continue to fuel legacy brands’ shift online and independent labels’ move into a brick-and-mortar presence.
E-commerce in beauty nearly quadrupled between 2015 and 2022, and its share now exceeds 20 percent, with significant runway ahead. This compares with a 2022 e-commerce share of approximately 30 percent in apparel and footwear, and around 65 percent in toys and games.
A number of factors have fueled e-commerce growth in beauty: the expansion of beauty offerings from online giants like Amazon in the United States and Tmall in China; the increased digital sophistication from direct-to-consumer players; the steadily growing significance of online for omnichannel retailers; and the proliferation of social selling, including livestreaming, in Asia. E-commerce is expected to continue to be the fastest-growing sales channel, at 12 percent per year between 2022 and 2027, but growth in traditional channels—including specialty retail, grocery retail, and drugstores—is expected to pick up postpandemic, as consumers’ preference for omnichannel is partly driven by their continued desire for in-store discovery and trial of products (Exhibit 2). Department stores are expected to continue to lose market share globally.
Structural and competitive dynamics are shifting
Where to play will become just as important a question as how to win, given the changing underlying growth tailwinds. The changing dynamics will render the industry’s largely homogenous global playbooks of the past decades less effective and require brands to reassess their global strategies and introduce greater nuance and tailoring.
Geographic diversification will become more essential than ever. It was just recently, for example, that brands could focus their footprints on the industry’s two top countries: China and the United States. Both countries will remain mighty forces for the industry, with the beauty market expected to reach $96 billion in China and $114 billion in North America by 2027 (Exhibit 3).
But in both markets, growth will be harder to come by for individual brands, not least due to fierce local and foreign competition. Meanwhile, other countries and regions, including the Middle East and India, are ready to step into the limelight, offering distinct potential for specific categories and price tiers. The likely upshot is that many brands will align their geographic strategies to this new world order, which will require a variety of localized playbooks.
Across geographies, another growth opportunity will be products and services in the top tier of the pricing pyramid: the true luxury and ultraluxury beauty market has the potential to double, from around $20 billion today to around $40 billion by 2027.
Five disruptive themes
The next few years will be a dynamic time for the beauty industry, filled with opportunities and new challenges. Its high profitability, with EBITDA margins of up to 30 percent, will continue to attract new founders and investors to the space. With limited spots available on the beauty palette, successful brands will adapt to the changing rules of the game and secure a uniquely differentiated value proposition amid a saturated market and increasingly sophisticated consumers. Key dynamics will include the following:
- The redrawing of the growth map. Slowing growth in China, along with increased local competition, means the country will no longer be a universal growth engine for the industry. As a result, the US market will become even more important, with strong growth, especially over the next few years. This market will become a competitive battleground for established brands and a potential green pasture for new entrants. The Middle East is expected to fuel growth over the same period, with India expected to emerge as a new hot spot in the longer term.
- The rise of wellness. As consumers are increasingly engaging with beauty products and services to not only look good but also feel good, the lines between beauty and wellness are expected to continue blurring, with the combined opportunity representing close to $2 trillion globally for brands, retailers, and investors. Wellness-inspired products—such as skincare and makeup with probiotic and Ayurvedic ingredients, ingestible supplements, and beauty devices like LED face masks—have already captured the attention of consumers embracing greater self-care and mindfulness in their postpandemic daily routines. The melding of wellness and beauty will only become more pronounced in the years ahead, in line with an expected CAGR of 10 percent to 2027 for the wellness industry. This trend will represent an untapped opportunity for many, with first-mover advantage for the players that get it right.
- The influence of Gen Z. Gen Zers scrutinize brands as part of their search for value. Nearly half of Gen Z respondents in our survey report conducting extensive research on product ingredients and their benefits before purchase, similar to millennials (and compared with only one-third of Gen Xers and one-fifth of baby boomers). Beyond product efficacy and transparency, Gen Zers demand that brands credibly stand for something. In addition to their focus on sustainability, diversity, and inclusion, Gen Zers greatly value brands that have an authentic and approachable image and a story that goes beyond products, and that welcome consumers into a wider community. Engaging with beauty products and services to feel good and express their authentic selves rather than adhering to specific cultural ideals, this cohort is challenging norms not only around the definition of physical beauty but also around gender and product categories.
- The imperative to scale. While the past decade has seen a number of new and independent labels benefit from steadily lower barriers to entry, growth beyond a successful initial run to achieve meaningful scale remains elusive for many. Out of 46 brands founded in or after 2005 with global retail sales of $50 million to $200 million by 2017, only five exceeded $250 million in global retail sales five years later, in 2022. Only two achieved global retail sales of more than $750 million. To scale successfully, brands must focus on omnichannel expansion and internationalization. Category expansion appears to be most effective when a brand has grown to a certain size, and when the expansion enhances and protects the brand’s unique value proposition.
- The recalibration of M&A. Amid continuously increasing interest in the beauty industry from a variety of players—from “strategics” to private equity funds—M&A will continue to play a major role in the industry. As seen in recent years, conglomerates and financial investors alike will pursue deals to invest in promising brands. But dealmaking will not be the same as when cost of capital was low. In the near term, megadeals will likely be few and far between in response to market turbulence. In addition, criteria for M&A targets will shift from a focus on high-growth independent “brands of the moment” to brands with an innovative product pipeline and a demonstrated ability to grow profitably, sustainably, and over the long term.
The years ahead will offer all the right ingredients—from agile channel mixes to consumers eager to explore new products—for the beauty industry’s continued growth. For beauty leaders and challengers alike, there will be plenty of opportunities to flourish, if they develop and execute tailored strategies that reflect the changing world of beauty.
Download the full report on which this article is based, The State of Fashion: Beauty (PDF–10MB).
Achim Berg is a senior partner in McKinsey’s Frankfurt office, Sara Hudson is a partner in the London office, Kristi Klitsch Weaver is a senior partner in the Chicago office, Megan Lesko Pacchia is a partner in the New Jersey office, and Imran Amed is the founder, editor in chief, and CEO of the Business of Fashion and an alumnus of McKinsey’s London office.
The authors wish to thank Anita Balchandani, Dimpy Jindal, Natalia Lepasch, Amaury Saint Olive, Alexis Wolfer, Alex Workman, and Andreas Zampouridis for their contributions to this article.
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For many, a 'natural death' may be preferable to enduring cpr.
Clayton Dalton
"Nurse refuses to perform CPR," read the caption on an ABC newscast in California. "911 dispatcher's pleas ignored." Several days earlier, an elderly woman at a senior living facility had gone into cardiac arrest. The dispatcher instructed an employee to perform CPR, or cardiopulmonary resuscitation. But the employee refused.
"Is there anybody there that's willing to help this lady and not let her die?" the dispatcher said. It made the local news, which elicited a national outcry and prompted a police investigation. But the woman was already dead — her heart had stopped. And according to family, the woman had wished to "die naturally and without any kind of life-prolonging intervention."
So why the controversy? It comes down to a widespread misconception of what CPR can, and can't, do. CPR can sometimes save lives, but it also has a dark side.
The discovery that chest compression could circulate blood during cardiac arrest was first reported in 1878, from experiments on cats. It wasn't until 1959 that researchers at Johns Hopkins applied the method to humans. Their excitement at its simplicity was clear: "Anyone, anywhere, can now initiate cardiac resuscitative procedures," they wrote. "All that is needed is two hands."
In the 1970s, CPR classes were developed for the public, and CPR became the default treatment for cardiac arrest. Flight attendants, coaches, and babysitters are now often required to be certified. The allure of CPR is that "death, instead of a final and irrevocable passage, becomes a process manipulable by humans," writes Stefan Timmermans, a sociologist who has studied CPR.
Why writing a will and planning for your death is a 'lifetime gift' to loved ones
"This is the truest of emergencies and you give people the simplest of procedures," Timmermans told me. "It seems too good to be true," he said, and it is.
Many people learn what they know about CPR from television . In 2015, researchers found that survival after CPR on TV was 70%. In real life, people similarly believe that survival after CPR is over 75%. Those sound like good odds, and this may explain the attitude that everyone should know CPR, and that everyone who experiences cardiac arrest should receive it. Two bioethicists observed in 2017 that "CPR has acquired a reputation and aura of almost mythic proportions," such that withholding it might appear "equivalent to refusing to extend a rope to someone drowning."
But the true odds are grim. In 2010 a review of 79 studies , involving almost 150,000 patients, found that the overall rate of survival from out-of-hospital cardiac arrest had barely changed in thirty years. It was 7.6%.
Bystander-initiated CPR may increase those odds to 10%. Survival after CPR for in-hospital cardiac arrest is slightly better, but still only about 17%. The numbers get even worse with age. A study in Sweden found that survival after out-of-hospital CPR dropped from 6.7% for patients in their 70s to just 2.4% for those over 90. Chronic illness matters too. One study found that less than 2% of patients with cancer or heart, lung, or liver disease were resuscitated with CPR and survived for six months.
But this is life or death — even if the odds are grim, what's the harm in trying if some will live? The harm, as it turns out, can be considerable. Chest compressions are often physically, literally harmful. "Fractured or cracked ribs are the most common complication," wrote the original Hopkins researchers, but the procedure can also cause pulmonary hemorrhage, liver lacerations, and broken sternums. If your heart is resuscitated, you must contend with the potential injuries.
A rare but particularly awful effect of CPR is called CPR-induced consciousness: chest compressions circulate enough blood to the brain to awaken the patient during cardiac arrest, who may then experience ribs popping, needles entering their skin, a breathing tube passing through their larynx.
The traumatic nature of CPR may be why as many as half of patients who survive wish they hadn't received it, even though they lived.
It's not just a matter of life or death, if you survive, but quality of life. The injuries sustained from the resuscitation can sometimes mean a patient will never return to their previous selves. Two studies found that only 20-40% of older patients who survive CPR were able to function independently; others found somewhat better rates of recovery.
An even bigger quality of life problem is brain injury. When cardiac activity stops, the brain begins to die within minutes, while the rest of the body takes longer. Doctors are often able to restart a heart only to find that the brain has died. About 30% of survivors of in-hospital cardiac arrest will have significant neurologic disability.
Again, older patients fare worse. Only 2% of those over 85 who suffer cardiac arrest survive without significant brain damage.
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CPR can be harmful not just for patients, but also for medical providers. In 2021, a study found that 60% of providers experienced moral distress from futile resuscitations, and that these experiences were associated with burnout. Another study linked intrusive memories and emotional exhaustion to difficult resuscitations. Holland Kaplan, a physician and bioethicist, told me that "the bad experiences far outnumber the good ones, unfortunately."
She has written about performing chest compressions on a frail, elderly patient and feeling his ribs crack like twigs. She found herself wishing she were "holding his hand in his last dying moments, instead of crushing his sternum." She told me that she's had nightmares about it. She described noticing his eyes, which were open, while she was performing CPR. Blood spurted out of his endotracheal tube with each compression.
"I felt like I was doing harm to him," she told me. "I felt like he deserved a more dignified death." It's no wonder that many doctors are not fond of CPR, and choose not to receive it themselves.
The true purpose of CPR is to "bridge the person to an intervention," Jason Tanguay, an emergency physician, told me. "If they can't get it, or there isn't one, then what is it accomplishing?" This is the crucial insight that doctors have and most others don't. CPR is a bridge, nothing more. Sometimes it spans the distance between life and death, if the cause can be quickly reversed, and if the patient is fairly young and relatively healthy. But for many that distance is too great. "The act of resuscitation itself cannot be expected to cure the inciting disease," the Hopkins researchers wrote in 1961.
A patient with terminal cancer who is resuscitated will still have terminal cancer. In those cases, the most humane approach may be to ease the pain of the dying process, rather than build a bridge to nowhere.
How can physicians help patients make these choices in advance? Part of it is education. Studies have found that half of patients changed their wishes when they learned the true survival rates of CPR, or after watching a video depicting the reality of CPR.
Another part is communication. According to one survey , 92% of Americans believe it's important to discuss end-of-life care, but only 32% have done so. Physicians (or patients) should initiate these conversations early, especially for those who are elderly or have chronic medical problems, so that their wishes are known in advance if they suffer a cardiac arrest.
Language matters too. Doctors often ask if patients "want everything done" if their heart stops. But that puts a burden on patients and families. "Who wants to feel like they don't want everything done for their loved one?" Kaplan says. Instead, if CPR would likely be futile, doctors could recommend "allow natural death" instead of "do not resuscitate," suggests Ellen Goodman, director of a non-profit that encourages end-of-life conversations.
"Give people something they can say yes to," she told me. Physicians have the knowledge and experience to guide patients in choosing measures they may benefit from, declining those that may harm, and aligning interventions with their wishes and values. The most important thing, instead of always taking action, is to ask.
Clayton Dalton is a writer in New Mexico, where he works as an emergency physician.
- advance directive
- death and dying
- cardiac arrest
IMAGES
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Part 1 Formatting Your Report 1 Look at other statistical reports. If you've never written a statistical report before, you might benefit from looking at other statistical reports that you can use as a guide to format your own. [2] You also get a good idea of how your finished report should look. [3]
Step 1: Write your hypotheses and plan your research design Step 2: Collect data from a sample Step 3: Summarize your data with descriptive statistics Step 4: Test hypotheses or make estimates with inferential statistics Step 5: Interpret your results Step 1: Write your hypotheses and plan your research design
Reporting Statistics in APA Style | Guidelines & Examples Published on April 1, 2021 by Pritha Bhandari . Revised on November 28, 2022. The APA Publication Manual is commonly used for reporting research results in the social and natural sciences. This article walks you through APA Style standards for reporting statistics in academic writing.
Writing with Descriptive Statistics Usually there is no good way to write a statistic. It rarely sounds good, and often interrupts the structure or flow of your writing. Oftentimes the best way to write descriptive statistics is to be direct.
This document gives general guidance about the writing of reports, with par-ticular reference to statistical reports following a statistical analysis. While this is immediately relevant for some of the courses you are taking, it may also be of help to you in your future job(s), some of which will hopefully use statistics. 2 Introduction
1. HR Statistics Report Template 2. Descriptive Statistics Report 10+ Statistics Report Examples 1. HR Statistics Report Template Details File Format MS Word Google Docs Apple Pages Size: 50 KB Download 2. Descriptive Statistics Report facstaff.bloomu.edu Details File Format PDF Size: 46 KB Download 3. Diabetes Statistics Report cdc.gov Details
The first thing you need is a good statistics reports example. If your tutor did not provide you with such samples, refer to the libraries or search for the data online. Choose an example of the statistical report or analysis which belongs to the same field that of study you work with.
EXAMPLES Reporting a significant correlation: Hours spent studying and GPA were strongly positively correlated, r(123) = .61, p = .011. Hours spent playing video games and GPA were moderately negatively correlated, r(123) = .32, p = .041. Reporting a significant single sample t-test (µ ≠ µ0):
Types of reports There are a few different types of reports, depending on the purpose and to whom you present your report. Here's a quick list of the common types of reports: Academic report: Tests a student's comprehension of the subject matter, such as book reports, reports on historical events, and biographies
The examples of topics to let the students realize how to write a statistical report are given in the article: Norway is the most prosperous economies in Europe based on research
This book contains five chapters which deals with the following topics: Report as a Form of Communication, The Structural Contents of a Report, Tables in a Report, Graphs and Charts Use in...
How to Write a Statistical Report Example The best way to start your statistical report isn't too different from other written assignments you've created in your life as a student.
The "Statistical Analyses and Methods in the Published Literature" ( SAMPL) guidelines covers basic statistical reporting for research in biomedical journals. General. PLOS ONE guidelines for statistical reporting. While specific to PLOS ONE, these guidelines should be applicable to most research contexts since the journal serves many ...
See sample statistical report here.https://csecenglishmadeeasy.com/2018/05/how-to-write-a-statistical-report-2/Welcome to the 2018 CSEC English Made Easy Exa...
For example, if Group A consisted of 10 mice and Group B consisted of 13 mice, do not report the sample size as "n=10-13 mice". Instead, clearly specify the precise sample size of each group (i.e., Group A, n=10 mice; Group B, n=13 mice). Statistical test: State the statistical test used. If applicable, indicate whether the test was one- or ...
An essay. A lab report in a science class. The overall structure of a data analysis report is simple: Introduction Body Conclusion(s)/Discussion Appendix/Appendices The data analysis report is written for several different audiences at the same time: Primary audience: A primary collaborator or client. Reads theIntroduction and perhaps the Conclusion
Double-Check Everything. The whole point of using data analytics tools and data, in general, is to achieve as much accuracy as possible. Avoid manual mistakes by proofreading your report when you finish, and if possible, give it to another person so they can confirm everything's in place.
First, make sure that you understand the different types of descriptive statistics and when to use each one. For example, mean, median, and mode are all measures of central tendency, but each one is used in different situations. Second, always report both the numeric values and graphical representations of your data.
Reporting of statistical analysis is essential in any clinical and translational research study. However, medical research studies sometimes report statistical analysis that is either inappropriate or insufficient to attest to the accuracy and validity of findings and conclusions. Published works involving inaccurate statistical analyses and insufficient reporting influence the conduct of ...
Examples of How to Write a Statistical Data Analysis Report The best way to start your paper is to write an abstract with 200 words. This part of the paper will include the basic info the whole paper, pointing out main points, goals and the target readers.
The State of Fashion: Beauty. Full Report (83 pages) In 2022, the beauty market—defined as skincare, fragrance, makeup, and haircare—generated approximately $430 billion in revenue. Today, beauty is on an upward trajectory across all categories. It has proven to be resilient amid global economic crises and in a turbulent macroeconomic ...
In 2010 a review of 79 studies, involving almost 150,000 patients, found that the overall rate of survival from out-of-hospital cardiac arrest had barely changed in thirty years. It was 7.6% ...