Related Experiment Video
Updated: Dec 18, 2025

Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
Published on: September 11, 2021
The Interpretation of Statistical Power after the Data have been Gathered
John J Dziak1, Lisa C Dierker1,2, Beau Abar3
1The Methodology Center, The Pennsylvania State University, University Park, PA, USA.
Abstract:
Post-hoc power estimates (power calculated for hypothesis tests after performing them) are sometimes requested by reviewers in an attempt to promote more rigorous designs. However, they should never be requested or reported because they have been shown to be logically invalid and practically misleading. We review the problems associated with post-hoc power, particularly the fact that the resulting calculated power is a monotone function of the p-value and therefore contains no additional helpful information. We then discuss some situations that seem at first to call for post-hoc power analysis, such as attempts to decide on the practical implications of a null finding, or attempts to determine whether the sample size of a secondary data analysis is adequate for a proposed analysis, and consider possible approaches to achieving these goals. We make recommendations for practice in situations in which clear recommendations can be made, and point out other situations where further methodological research and discussion are required.
Related Concept Videos
Statistical Significance
Errors In Hypothesis Tests
Bioequivalence Data: Statistical Interpretation
Accuracy and Errors in Hypothesis Testing
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
Statistical Analysis: Overview
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
P-value
P-value stands for the probability value. P-value is the probability that, if the null hypothesis is true, the results from another randomly selected sample will be as extreme or more extreme as the results obtained from the given sample.
A large P-value calculated from the data indicates to not reject the null hypothesis. But a higher P-value does not mean that the null hypothesis is true. The smaller the P-value, the more...

