{"id":3960,"date":"2022-08-04T13:16:16","date_gmt":"2022-08-04T13:16:16","guid":{"rendered":"https:\/\/www.editage.com\/insights\/statistical-reporting-doesnt-have-to-be-scary-tips-for-the-statistically-challenged-researcher\/"},"modified":"2026-03-19T12:02:41","modified_gmt":"2026-03-19T06:32:41","slug":"statistical-reporting-doesnt-have-to-be-scary-tips-for-the-statistically-challenged-researcher","status":"publish","type":"post","link":"https:\/\/www.editage.com\/insights\/statistical-reporting-doesnt-have-to-be-scary-tips-for-the-statistically-challenged-researcher","title":{"rendered":"Statistical reporting doesn\u2019t have to be scary: Tips for the statistically challenged researcher"},"content":{"rendered":"<p style=\"margin-bottom: 0cm; margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"background: white;\"><span style=\"line-height: normal;\"><span style=\"font-family: Calibri,sans-serif;\"><span lang=\"EN-US\" style=\"color: #252423;\" xml:lang=\"EN-US\">In our current world, filled with a constant availability of information and opinion, trust and integrity are becoming more elusive. As scientists, we are committed to the truth, and we need to work to keep these words alive and relevant.<\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"margin-bottom: 0cm; margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"background: white;\"><span style=\"line-height: normal;\"><span style=\"font-family: Calibri,sans-serif;\"><span lang=\"EN-US\" style=\"color: #252423;\" xml:lang=\"EN-US\">On a global scale, the misrepresentation of the results of a drug study could theoretically cause harm to someone taking the drug. Although this would have to be an egregious and systemic error, the possibility is there<sup>1<\/sup>. <\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"margin-bottom: 0cm; margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"background: white;\"><span style=\"line-height: normal;\"><span style=\"font-family: Calibri,sans-serif;\"><span lang=\"EN-US\" style=\"color: #252423;\" xml:lang=\"EN-US\">On a more realistic scale, misinterpretation of research results causes the waste of time, money, and human resources and further erodes the public trust in science or scientists. It doesn\u2019t help the career of the researcher any either.<\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"margin-bottom: 0cm; margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"background: white;\"><span style=\"line-height: normal;\"><span style=\"font-family: Calibri,sans-serif;\"><span lang=\"EN-US\" style=\"color: #252423;\" xml:lang=\"EN-US\">While the proper use of statistics can be confusing, it should not be scary. This article provides some simple suggestions to help you be more thoughtful and accurate in analyzing and reporting your study results, even if statistics isn\u2019t one of your strengths.<\/span><\/span><\/span><\/span><\/span><\/p>\n<h1 style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 16pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: 'Calibri Light',sans-serif;\"><span style=\"color: #2f5496;\"><span style=\"font-weight: normal;\">Reasons researchers may make mistakes <\/span><\/span><\/span><\/span><\/span><\/h1>\n<p style=\"margin-bottom: 0cm; margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"background: white;\"><span style=\"line-height: normal;\"><span style=\"font-family: Calibri,sans-serif;\"><span lang=\"EN-US\" style=\"color: #252423;\" xml:lang=\"EN-US\">Statistics is a strange discipline that promotes apprehension in a lot of people. Those who have an aversion to math are scared by all the numbers, while at the same time, math proponents are put off by the inexact nature of statistics. Statistics involves a lot of numbers but does not offer definite right answers, such as one finds in algebra or calculus.<\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"margin-bottom: 0cm; margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"background: white;\"><span style=\"line-height: normal;\"><span style=\"font-family: Calibri,sans-serif;\"><span lang=\"EN-US\" style=\"color: #252423;\" xml:lang=\"EN-US\">A fear of statistics is one of the major reasons researchers make errors in analyzing and reporting their study data and results. The consequence of this aversion is typically a desire to avoid all things statistical and not educate oneself enough. This can lead to being sloppy and not paying enough attention to being accurate and confident with the study results. Of course, being frightened of the numbers is not the only reason researchers make statistical mistakes. Other causes may include the following:<\/span><\/span><\/span><\/span><\/span><\/p>\n<ul>\n<li style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"background: white;\"><span style=\"line-height: normal;\"><span style=\"font-family: Calibri,sans-serif;\"><span class=\"Heading2Char\" style=\"font-family: 'Calibri Light',sans-serif;\"><span style=\"color: #2f5496;\"><span lang=\"EN-US\" style=\"font-size: 13.0pt;\" xml:lang=\"EN-US\">Lack of formal training<\/span><\/span><\/span><span lang=\"EN-US\" style=\"color: #252423;\" xml:lang=\"EN-US\"> \u2013 Some researchers may not have received sufficient formal training in statistics, although the numerous classes and websites on the internet are now making it possible to overcome this deficit through self-learning.<\/span><\/span><\/span><\/span><\/span><\/li>\n<li style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"background: white;\"><span style=\"line-height: normal;\"><span style=\"font-family: Calibri,sans-serif;\"><span class=\"Heading2Char\" style=\"font-family: 'Calibri Light',sans-serif;\"><span style=\"color: #2f5496;\"><span lang=\"EN-US\" style=\"font-size: 13.0pt;\" xml:lang=\"EN-US\">Lack of support<\/span><\/span><\/span><span lang=\"EN-US\" style=\"color: #252423;\" xml:lang=\"EN-US\"> \u2013 If you\u2019re not strong in statistics, you need to find someone who is and can check your work. That may not be possible if that support is not available.<\/span><\/span><\/span><\/span><\/span><\/li>\n<li style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"background: white;\"><span style=\"line-height: normal;\"><span style=\"font-family: Calibri,sans-serif;\"><span class=\"Heading2Char\" style=\"font-family: 'Calibri Light',sans-serif;\"><span style=\"color: #2f5496;\"><span lang=\"EN-US\" style=\"font-size: 13.0pt;\" xml:lang=\"EN-US\">Lack of resources<\/span><\/span><\/span><span lang=\"EN-US\" style=\"color: #252423;\" xml:lang=\"EN-US\"> \u2013 Researchers who have limited amounts of time or funding may find it difficult to spend enough attention on the data collection and analysis, which may then lead to errors. For example, a lack of time may result in verifications not being done, and a lack of funding may mean an inadequate number of personnel for the project<sup>1<\/sup>.<\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n<h1 style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 16pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: 'Calibri Light',sans-serif;\"><span style=\"color: #2f5496;\"><span style=\"font-weight: normal;\">8 avoidable statistical errors made by researchers<\/span><\/span><\/span><\/span><\/span><\/h1>\n<p style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\"><span lang=\"EN-US\" style=\"color: #252423;\" xml:lang=\"EN-US\">Statistical errors can occur in various steps of the research process, including during the data collection, analysis, and reporting. Here are some fundamental errors the statistically challenged researcher should be aware of to avoid inaccurate reporting.<\/span><\/span><\/span><\/span><\/p>\n<ul>\n<li style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\"><span class=\"Heading2Char\" style=\"font-family: 'Calibri Light',sans-serif;\"><span style=\"color: #2f5496;\"><span lang=\"EN-US\" style=\"font-size: 13.0pt;\" xml:lang=\"EN-US\"><span style=\"line-height: 105%;\">Not using clean data<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n<p style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\">This error is usually not obvious to readers unless they look at the actual data, which is one reason for the increasing journal requirement of data transparency. Datasets that contain outliers and missing values need to be treated carefully prior to analysis. If you\u2019re reporting on the average height of college freshmen, and a quarter of the records in your data are blank, or if several of the students are on the basketball team, you likely won\u2019t get an accurate average. The solution to this is to simply get to know your data. Create some graphs: scatter plots, histograms, whatever will help your data show what they look like.<\/span><\/span><\/span><\/p>\n<ul>\n<li style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\"><span class=\"Heading2Char\" style=\"font-family: 'Calibri Light',sans-serif;\"><span style=\"color: #2f5496;\"><span lang=\"EN-US\" style=\"font-size: 13.0pt;\" xml:lang=\"EN-US\"><span style=\"line-height: 105%;\">Adopting a poor sampling method<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n<p style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\">This issue shows up frequently in studies involving survey data. The underlying mistake is assuming that the data collected represent the population of interest. However, with surveys, it is very difficult to get a true representative sample. For instance, to obtain the required number of surveys, researchers often opt for the most convenient method. That may mean standing outside a supermarket or calling home phone numbers. Using either of these common methods will skew the resulting data. When collecting your data, think carefully about your population of interest and what population you are actually sampling.<\/span><\/span><\/span><\/p>\n<ul>\n<li style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\"><span class=\"Heading2Char\" style=\"font-family: 'Calibri Light',sans-serif;\"><span style=\"color: #2f5496;\"><span lang=\"EN-US\" style=\"font-size: 13.0pt;\" xml:lang=\"EN-US\"><span style=\"line-height: 105%;\">Reporting spurious correlations<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n<p style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\">Be mindful about finding significant correlations between variables that are not actually related. For example, a strong correlation was found between the number of master\u2019s degrees conferred in the US and box office revenue<sup>2<\/sup>. Obviously, these variables are not closely related. <\/span><\/span><\/span><\/p>\n<ul>\n<li style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\"><span class=\"Heading2Char\" style=\"font-family: 'Calibri Light',sans-serif;\"><span style=\"color: #2f5496;\"><span lang=\"EN-US\" style=\"font-size: 13.0pt;\" xml:lang=\"EN-US\"><span style=\"line-height: 105%;\">Assuming that correlation implies causation<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n<p style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\">Evidence of a significant positive or negative correlation between variables is not evidence that a change in one causes a change in the other. A popular example of this is the strong positive correlation between shark attacks and ice cream consumption<sup>3<\/sup>. Does this correlation mean that shark attacks are caused by eating ice cream? Common sense will tell you that both are caused by a third factor, namely, summer.<\/span><\/span><\/span><\/p>\n<ul>\n<li style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\"><span class=\"Heading2Char\" style=\"font-family: 'Calibri Light',sans-serif;\"><span style=\"color: #2f5496;\"><span lang=\"EN-US\" style=\"font-size: 13.0pt;\" xml:lang=\"EN-US\"><span style=\"line-height: 105%;\">Using a wrong analysis method<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n<p style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\">You don\u2019t need to be fully immersed in statistical knowledge to avoid this mistake. Unless you have help, you probably won\u2019t be using any complex analysis methods on your data. However, you need to understand the methods you do use. For instance, can a simple linear regression be used on categorical variables? Can <i>t<\/i>-tests find significant differences between multiple groups? It will take a little work, but you can avoid some embarrassing mistakes by doing some reading.<\/span><\/span><\/span><\/p>\n<ul>\n<li style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\"><span class=\"Heading2Char\" style=\"font-family: 'Calibri Light',sans-serif;\"><span style=\"color: #2f5496;\"><span lang=\"EN-US\" style=\"font-size: 13.0pt;\" xml:lang=\"EN-US\"><span style=\"line-height: 105%;\">Failing to check analysis assumptions<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n<p style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\">This is related to the first error. All analysis methods are based on assumptions made about the data on which they are being used. For example, many methods are based on the assumption that the underlying data is normally distributed. If your data has a different distribution, the results may not be accurate. The solution is to make sure you fully understand the variables and data involved. Again, graphs can help tremendously here.<\/span><\/span><\/span><\/p>\n<ul>\n<li style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\"><span class=\"Heading2Char\" style=\"font-family: 'Calibri Light',sans-serif;\"><span style=\"color: #2f5496;\"><span lang=\"EN-US\" style=\"font-size: 13.0pt;\" xml:lang=\"EN-US\"><span style=\"line-height: 105%;\">Not correctly reporting results<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n<p style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\">A frequent error made by statistically challenged researchers is not correctly interpreting and reporting the results they get, often overstating their results. This may mean they write that they proved something, when the results only indicate that the something may be true in one small specific case. Statistics is a very precise and conservative language. If your results suggest that your null hypothesis cannot be rejected, it doesn\u2019t mean that it is proven. It only means that it cannot be disproven, and your results can only provide evidence supporting that hypothesis. This can be very confusing to even statistically minded researchers.<\/span><\/span><\/span><\/p>\n<ul>\n<li style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\"><span class=\"Heading2Char\" style=\"font-family: 'Calibri Light',sans-serif;\"><span style=\"color: #2f5496;\"><span lang=\"EN-US\" style=\"font-size: 13.0pt;\" xml:lang=\"EN-US\"><span style=\"line-height: 105%;\">Cherry-picking data and results<\/span><\/span><\/span><\/span><\/span><\/span><\/span><\/li>\n<\/ul>\n<p style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\">It is never a good idea to conduct a study already knowing what results you need. This may lead to manipulating the data or finding a test that will produce the desired result. Another not-completely-ethical approach is to develop the hypothesis after the data is obtained and examined<sup>4<\/sup>. <\/span><\/span><\/span><\/p>\n<p style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\">Most of these errors can be avoided by applying common sense and paying attention. However, many more complex mistakes frequently appear in research studies. As always, the best solution is to consult a friend or colleague who has a lot of statistical knowledge and experience.<\/span><\/span><\/span><\/p>\n<p style=\"margin: 0cm 0cm 8pt;\"><i>Would you like a 1:1 consultation with an expert statistician? Check out Editage\u2019s\u00a0<\/i><a href=\"https:\/\/www.editage.com\/services\/publishing-services-packs\/statistical-analysis\"><i>Statistical Analysis &amp; Review Service<\/i><\/a><\/p>\n<p style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 16pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: 'Calibri Light',sans-serif;\"><span style=\"color: #2f5496;\"><span style=\"font-weight: normal;\">References<\/span><\/span><\/span><\/span><\/span><\/p>\n<p style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\">1. Brown AW, Kaiser KA, and Allison DB. Issues with data and analyses: Errors, underlying themes, and potential solutions. <i>PNAS.<\/i> 2018, 115, 2563\u201370. https:\/\/doi.org\/10.1073\/pnas.1708279115<\/span><\/span><\/span><\/p>\n<p style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\">2. Statology. 5 examples of spurious correlation in real life. <a style=\"color: #0563c1; text-decoration: underline;\" href=\"https:\/\/www.statology.org\/spurious-correlation-examples\/\">https:\/\/www.statology.org\/spurious-correlation-examples\/<\/a> [Accessed 29 July 2022]<\/span><\/span><\/span><\/p>\n<p style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\">3. Statology. Correlation does not imply causation: 5 real-world examples. <a style=\"color: #0563c1; text-decoration: underline;\" href=\"https:\/\/www.statology.org\/correlation-does-not-imply-causation-examples\/\">https:\/\/www.statology.org\/correlation-does-not-imply-causation-examples\/<\/a> [Accessed 29 July 2022]<\/span><\/span><\/span><\/p>\n<p style=\"margin: 0cm 0cm 8pt;\"><span style=\"font-size: 11pt;\"><span style=\"line-height: 105%;\"><span style=\"font-family: Calibri,sans-serif;\">4. Gray K. Statistical mistakes even scientists make. KDnuggets. <a style=\"color: #0563c1; text-decoration: underline;\" href=\"https:\/\/www.kdnuggets.com\/2017\/10\/statistical-mistakes-even-scientists-make.html\">https:\/\/www.kdnuggets.com\/2017\/10\/statistical-mistakes-even-scientists-make.html<\/a> [Accessed 29 July 2022]<\/span><\/span><\/span><\/p>\n<p style=\"margin: 0cm 0cm 8pt;\">\n","protected":false},"excerpt":{"rendered":"<p>In our current world, filled with a constant availability of information and opinion, trust and integrity are becoming more elusive. As scientists, we are committed to the truth, and we need to work to keep these words alive and relevant. On a global scale, the misrepresentation of the results of a drug study could theoretically [&hellip;]<\/p>\n","protected":false},"author":2704,"featured_media":45150,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[2420],"tags":[2622,2591,1319,366],"new_categories":[],"new_tags":[],"series":[],"class_list":["post-3960","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-data-analysis","tag-analysisofdata","tag-data-presentation","tag-statistical-analysis","tag-statistical-reporting"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Statistical reporting doesn\u2019t have to be scary: Tips for the statistically challenged researcher<\/title>\n<meta name=\"description\" content=\"While the proper use of statistics can be confusing, it should not be scary. 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