{"id":235615,"date":"2024-05-27T09:05:18","date_gmt":"2024-05-27T09:05:18","guid":{"rendered":"https:\/\/namso-gen.co\/blog\/?p=235615"},"modified":"2024-05-27T09:05:18","modified_gmt":"2024-05-27T09:05:18","slug":"how-to-calculate-effect-size-from-p-value","status":"publish","type":"post","link":"https:\/\/namso-gen.co\/blog\/how-to-calculate-effect-size-from-p-value\/","title":{"rendered":"How to calculate effect size from p-value?"},"content":{"rendered":"

When conducting statistical analysis, it’s crucial to not only look at the significance of the results but also the practical significance, which can be quantified using effect size. Effect size helps researchers understand the magnitude of the relationship between variables or the impact of an intervention. P-value, on the other hand, only tells us if the results are statistically significant.<\/p>\n

**To calculate effect size from p-value, you will need more information than just the p-value. Typically, effect size is calculated using statistical measures such as Cohen’s d, odds ratio, or correlation coefficient, which can be derived from the raw data or additional statistical tests.**<\/p>\n

Effect size provides a more comprehensive understanding of the results and is particularly useful when comparing different studies or interventions. In this article, we will delve deeper into the concept of effect size, its importance, and how to calculate it from p-values.<\/p>\n

\n
\n

Table of Contents<\/p>\nToggle<\/span><\/path><\/svg><\/svg><\/span><\/span><\/span><\/a><\/span><\/div>\n