{"id":1004,"date":"2016-05-11T13:21:00","date_gmt":"2016-05-11T13:21:00","guid":{"rendered":"https:\/\/www.editage.com\/insights\/is-my-research-significant-why-you-shouldnt-rely-on-p-values\/"},"modified":"2026-05-17T21:03:50","modified_gmt":"2026-05-17T15:33:50","slug":"is-my-research-significant-why-you-shouldnt-rely-on-p-values","status":"publish","type":"post","link":"https:\/\/www.editage.com\/insights\/is-my-research-significant-why-you-shouldnt-rely-on-p-values","title":{"rendered":"How to use p values correctly in a research paper"},"content":{"rendered":"

The most commonly reported statistic in research papers may also be the most misunderstood and misused. We\u2019re talking about the p value.<\/p>\n

The American Statistical Association<\/a> (ASA) has released the \u201cStatement on Statistical Significance and P-Values<\/a>,\u201d outlining six principles pertaining to appropriate use and interpretation of p values.<\/p>\n

Let\u2019s take a look at the ASA principles, and how they apply to research.<\/p>\n

What do p values indicate?<\/h2>\n

1. P values can indicate how incompatible the data are with a specified statistical model.<\/strong><\/p>\n

Here, the important word is \u201cspecified.\u201d Remember that in any study or analysis, the researchers are bound to have made certain assumptions when creating a statistical model. According to statisticians<\/a>, a\u00a0p\u00a0value of 0.05 does not mean that there is a 95% chance that a given hypothesis is correct. Instead, this value means that if the null hypothesis<\/a> is true and if all other assumptions made are valid, we have a 5% chance of obtaining a result at least as large as the result currently obtained.<\/p>\n

What do p values NOT measure?<\/h2>\n

2. P values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone.<\/strong><\/p>\n

Researchers often erroneously interpret smaller p\u00a0values to mean that the null hypothesis is false. Actually, p values only indicate the probability of obtaining results at least as large as those observed if the null hypothesis was true.<\/a><\/p>\n

Should p values be used for decision making?<\/h2>\n

3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.<\/strong><\/p>\n

\u201cP < .05\u201d is not a guarantee that something is true. Ultimately, a p value is just a statistic and not a sign from heaven. A p value can be influenced by many aspects of a study, especially sample size. If a sample is particularly large, the p value may almost always be significant<\/a> (though the effect size may be negligible), unless there is absolutely no effect. Hence, it\u2019s common sense that you can\u2019t make a practical decision on the basis of a p value alone.<\/p>\n

Should all p values be reported in a research paper?<\/h2>\n

4. Proper inference requires full reporting and transparency.<\/strong><\/p>\n

Often, the only results reported are those with p value below .05. The ASA strongly discourages this kind of \u201ccherry picking<\/a>.\u201d Instead, it recommends that authors report all hypotheses explored, all statistical analyses conducted, and all p-values obtained, whether significant or not. Only then can authors draw valid conclusions on the basis of their data. Authors can take the help of\u00a0professional publication support services, for example, Editage\u2019s Statistical Review Service<\/a>,\u00a0to ensure robust analyses and and get their statistical analyses reviewed.<\/p>\n

Does a p value indicate the size of an effect or importance of a result?<\/h2>\n

5. A p-value, or statistical significance, does not measure the size of an effect or the importance of a result.<\/strong><\/p>\n

Some authors label findings with a very low p value (<.001) as \u201cvery significant\u201d or \u201chighly significant.\u201d However, a low p value does not mean that the result is of practical or clinical importance.<\/p>\n

Let\u2019s assume you\u2019ve found a statistically significant relationship between increased energy drink consumption and positive body image in girls. This doesn\u2019t mean that you should design an intervention to improve body image in which girls are given free energy drinks! Instead, you should look at the strength<\/em> of the relationship (e.g., correlation coefficient, regression coefficient). If the relationship is weak (e.g., a correlation coefficient of just 0.1), your intervention will probably be more effective if you consider other factors that have a stronger relationship with body image (e.g., general self-esteem, frequency of fat talk).<\/p>\n

It\u2019s also important to consider context when determining the importance of a result. Small differences between large groups can be statistically significant but unimportant practically, while large differences between small groups can be important in practical terms even if they are not statistically significant<\/a>. For example, a mean increase of 1.5 points in scores on a 100-point math test after an educational intervention may be statistically significant, but the intervention itself may not be particularly beneficial or useful in real life.<\/p>\n

Is a p value a good measure of evidence?<\/h2>\n

6. By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis.<\/strong><\/p>\n

Authors should avoid reporting just p values in their results<\/a>. A small p value doesn\u2019t indicate that the null hypothesis is false, nor does a large p value mean that the null hypothesis is true. In research, there could be a variety of hypotheses that are just as consistent with the observed data. Hence, a p value is not the only form of statistical support for the model or theory being tested, and the value of a study does not depend solely on the p values found.<\/p>\n

In summary, although p values can be useful, they are not the yardstick by which a study becomes valuable and important, and they should not be treated as such. Statistical significance is not the same as scientific, practical, or clinical significance.<\/p>\n

Related reading:<\/strong><\/p>\n

The correct way to report p values<\/a><\/p>\n

Importance of statistical power in research design<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"

The most commonly reported statistic in research papers may also be the most misunderstood and misused. We\u2019re talking about the p value. The American Statistical Association (ASA) has released the \u201cStatement on Statistical Significance and P-Values,\u201d outlining six principles pertaining to appropriate use and interpretation of p values. Let\u2019s take a look at the ASA […]<\/p>\n","protected":false},"author":15,"featured_media":33313,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[2405,2377],"tags":[1803,636],"new_categories":[],"new_tags":[],"series":[],"class_list":["post-1004","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-industry-trends","category-methods","tag-methods-section","tag-p-values"],"acf":[],"yoast_head":"\nHow to use p values correctly in a research paper | Editage Insights<\/title>\n<meta name=\"description\" content=\"Learn how to report and interpret p values correctly in a research paper.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" 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