{"id":1783,"date":"2013-10-16T12:35:30","date_gmt":"2013-10-16T12:35:30","guid":{"rendered":"https:\/\/www.editage.com\/insights\/the-correct-way-to-report-p-values\/"},"modified":"2026-03-16T10:18:42","modified_gmt":"2026-03-16T04:48:42","slug":"correct-way-report-p-values","status":"publish","type":"post","link":"https:\/\/www.editage.com\/insights\/correct-way-report-p-values","title":{"rendered":"P values in research: A handy guide"},"content":{"rendered":"<p>Few statistics are as misunderstood as p values. This article will tell you<\/p>\n<ul>\n<li><a href=\"#_What_are_p\">What are p values?<\/a><\/li>\n<li><a href=\"#_What_do_p\">What do p values indicate?<\/a><\/li>\n<li><a href=\"#_Should_researchers_report\">Should researchers report just p values?<\/a><\/li>\n<li><a href=\"#_Correct_Way_of\">Correct way of reporting p values<\/a><\/li>\n<li><a href=\"#_Errors_in_Reporting\">Errors in reporting p values<\/a><\/li>\n<\/ul>\n<h2>What are p values?<\/h2>\n<p>A p value is a statistic used in null hypothesis significance testing (NHST) and tells you the probability of getting the same result through chance. So if you have a result like (r = .64, p &lt; .001), it means that the <em>chance<\/em> of getting r = .64 due to luck is less than 1\/1000.<\/p>\n<h2>What do p values indicate?<\/h2>\n<p>In general, <em>p <\/em>values tell readers only whether any difference between groups, relationship, etc., is likely to be due to chance or to the variable(s) you are studying. According to most statistical guidelines, including those provided by Nature,<sup>1<\/sup> you need to provide a <em>p <\/em>value for any change, difference, or relationship called \u201csignificant.\u201d Further, because the significance threshold (i.e., the <em>p <\/em>value that you use as a cutoff for determining significance) can be .05, .001, or .01, it\u2019s advisable to state the significance threshold used in your research in the Methods section of your paper. A sentence like \u201cThe significance threshold was set at .05\u201d is all that is required.<\/p>\n<h2>Should researchers report just p values?<\/h2>\n<p>A <em>p <\/em>value cannot tell readers the strength or size of an effect, change, or relationship. Therefore, you should avoid reporting nothing else but <em>p <\/em>values. It\u2019s always a good idea to provide a test statistic (<em>t, F<\/em>, <em>U<\/em>, etc.), correlation or regression coefficient (Pearson\u2019s <em>r, <\/em>Spearman\u2019s rho, etc.), or measure of effect size (eta-squared, partial-eta-squared, omega-squared, etc.).<\/p>\n<h2>Correct Way of Reporting p Values<\/h2>\n<h3>Example 1<\/h3>\n<p>Let\u2019s take the example of the sentence \u201cWe found a significant relationship between anxiety and job satisfaction (<em>p<\/em> &lt; .05).\u201d Here, all you are telling the readers is that you have enough evidence that this relationship is unlikely to be due to chance. Readers don\u2019t know whether this relationship is direct or inverse (i.e., did participants with higher anxiety have higher job satisfaction or did participants with lower anxiety have higher job satisfaction?). Further, was this relationship strong or weak? For the benefit of the reader, you should also report a correlation coefficient along with the <em>p <\/em>value. If you add \u201c<em>r <\/em>= -.78\u201d in the parentheses at the end of the above sentence, your readers will understand that this is a strong inverse relationship. Thus, they get a better idea of your actual findings.<\/p>\n<h3>Example 2<\/h3>\n<p>Here\u2019s another example: \u201cWe found a significant difference between pretest and posttest scores.\u201d I would recommend reporting (a) the test statistic so that the reader knows what <a href=\"https:\/\/www.editage.com\/services\/publishing-services-packs\/statistical-analysis?utm_source=editageinsights&amp;utm_medium=qna&amp;utm_campaign=editage-insights-site-p-values--row\">statistical test<\/a> you performed to examine this difference and (b) a measure of effect size so that the reader understands how large this difference is. Even the mean pretest and posttest scores could be sufficient for readers to understand the size of the effect you have found.<\/p>\n<p>In addition, it\u2019s a good idea to report <em>exact p<\/em> values, since this practice makes for greater scientific integrity. In the above sentence, the <em>p <\/em>value could be \u201c.048\u201d; this value is technically below \u201c.05\u201d but so close to .05 that it would probably need to be treated like a <em>p <\/em>value of .51, which is not statistically significant. Typically, if the exact <em>p<\/em> value is less than .001, you can merely state \u201cp &lt; .001.\u201d Otherwise, report exact <em>p<\/em> values, especially for primary outcomes.<\/p>\n<h2>Errors in Reporting p Values<\/h2>\n<p>Furthermore, here are a couple of basic errors I\u2019ve come across with regard to <em>p <\/em>values:<\/p>\n<ol>\n<li><strong> \u201c<em>p<\/em> =\u00a0 .00\u201d or \u201c<em>p<\/em> &lt; .00\u201d<\/strong><\/li>\n<\/ol>\n<p>Technically, <em>\u00a0p<\/em> values cannot equal 0. Some statistical programs do give you <em>p<\/em> values of .000 in their output, but this is likely due to automatic rounding off or truncation to a preset number of digits after the decimal point. So, consider replacing &#8220;<em>p<\/em> = .000&#8243; with &#8220;<em>p<\/em> &lt; .001,&#8221; since the latter is considered more acceptable and does not substantially alter the importance of the <em>p<\/em> value reported. And <em>p <\/em>always lies between 0 and 1; it can never be negative.<\/p>\n<ol start=\"2\">\n<li><strong> \u201c<em>p<\/em> &lt; .03\u201d<\/strong><\/li>\n<\/ol>\n<p>Many journals accept <em>p<\/em> values that are expressed in relational terms with the alpha value (the statistical significance threshold), that is, \u201c<em>p<\/em> &lt; .05,\u201d \u201c<em>p<\/em> &lt; .01,\u201d or \u201c<em>p<\/em> &lt; .001.\u201d They can also be expressed in absolute values, for example, \u201c<em>p<\/em> = .03\u201d or \u201c<em>p <\/em>= .008.\u201d However, <em>p<\/em> values are conventionally not used with the greater than (&gt;) or less than (&lt;) sign when what follows the sign is not the alpha value.<\/p>\n<p><strong>One last tip: <\/strong>Many authorities in scientific, technical, and medical fields recommend that a zero should not be inserted before a decimal fraction when the number cannot be greater than 1 (e.g., correlations, proportions, and levels of statistical significance); that is, \u201c<em>p<\/em> &lt; 0.05\u201d should be written as \u201c<em>p<\/em> &lt; .05.\u201d<\/p>\n<p>Do write in a comment with any further questions you may have.<\/p>\n<p>You might find this course helpful: <a href=\"https:\/\/upskill.researcher.life\/product\/course-how-to-write-the-most-effective-results-and-discussion-sections\/91?utm_source=website&amp;utm_medium=internal_related_reading&amp;utm_campaign=article\">How to write the most effective results and discussion section<\/a><\/p>\n<h2>Frequently Asked Questions<\/h2>\n<h3>Should I report p values in the tables or text?<\/h3>\n<p>Most p values can be reported in your tables or figures. Avoid cluttering up the text with strings of p values. You may report p values for your 2-3 most important results, but that&#8217;s all.<\/p>\n<h3>To how many places should p values be reported?<\/h3>\n<p>You generally should report p values up to 2 decimal places. You nearly always don&#8217;t need to report p values to more than 3 places.<\/p>\n<h3>Can I use asterisks for p values?<\/h3>\n<p>Yes, it&#8217;s a common practice to have a series of asterisks (*, **, ***) to indicate p values, especially in tables and figures. Just be sure to always define these symbols in the figure legend\/table notes each time.<\/p>\n<h2>References<\/h2>\n<ol>\n<li>Nature Communications Statistical Guidelines <a href=\"https:\/\/www.nature.com\/documents\/ncomms_-_statisticalguidance.pdf\">https:\/\/www.nature.com\/documents\/ncomms_-_statisticalguidance.pdf<\/a><\/li>\n<\/ol>\n<h2>Recommended reading:<\/h2>\n<ul>\n<li><a href=\"https:\/\/www.editage.com\/insights\/optimizing-research-quality-importance-of-statistical-power-and-how-to-calculate-it-in-biomedical-sciences\/\">Importance of statistical power in research design<\/a><\/li>\n<li><a href=\"https:\/\/www.editage.com\/insights\/linking-statistical-significance-to-clinical-importance-of-trial-data-a-paradigm-shift\/\">Linking statistical significance to clinical importance of trial data: A paradigm shift<\/a><\/li>\n<\/ul>\n<p>&nbsp;<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Few statistics are as misunderstood as p values. This article will tell you What are p values? What do p values indicate? Should researchers report just p values? Correct way of reporting p values Errors in reporting p values What are p values? A p value is a statistic used in null hypothesis significance testing [&hellip;]<\/p>\n","protected":false},"author":15,"featured_media":45150,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"_acf_changed":false,"inline_featured_image":false,"footnotes":""},"categories":[2377,2427],"tags":[1803,636],"new_categories":[],"new_tags":[],"series":[],"class_list":["post-1783","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-methods","category-style-format","tag-methods-section","tag-p-values"],"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v25.0 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>P values in research: A handy guide | Editage Insights<\/title>\n<meta name=\"description\" content=\"Learn what are p values, how to report p values in a research paper, and what do p values indicate, and common errors researchers make.\" \/>\n<meta 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