Related Experiment Video
Updated: Jan 19, 2026
10:26
Problem-Solving Before Instruction PS-I: A Protocol for Assessment and Intervention in Students with Different Abilities
Published on: September 11, 2021
4.4K
Post hoc power analysis: is it an informative and meaningful analysis?
Yiran Zhang1, Rita Hedo1, Anna Rivera2,3
1Department of Family Medicine and Public Health, University of California System, Oakland, California, USA.
General Psychiatry
|September 26, 2019
Summary
Post hoc power analysis, often requested for retrospective studies, is examined. This study scrutinizes its informativeness for detecting effects using already analyzed data, employing Monte Carlo simulations.
Area of Science:
- Biomedical Sciences
- Psychosocial Sciences
- Statistical Analysis
Background:
- Power analysis is crucial for prospective study design, including clinical trials.
- Some journals require power analysis for retrospective studies using existing data.
- The utility of post hoc power analysis in retrospective research is debated.
Purpose of the Study:
- To examine the role and informativeness of post hoc power analysis in retrospective studies.
- To scrutinize the understanding of statistical power for detecting effects in already analyzed data.
- To investigate the performance of post hoc power analysis using Monte Carlo simulation.
Main Methods:
- Retrospective study design.
- Post hoc power analysis.
- Monte Carlo simulation.
Main Results:
- The informativeness of post hoc power analysis for retrospective studies was investigated.
- Performance of post hoc power analysis was evaluated using simulation.
Conclusions:
- Findings address the application and interpretation of post hoc power analysis in retrospective research.
- The study provides insights into the statistical power of detecting effects in existing datasets.
Related Concept Videos
01:50Statistical Significance
21.1K
Once data is collected from both the experimental and the control groups, a statistical analysis is conducted to find out if there are meaningful differences between the two groups. A statistical analysis determines how likely any difference found is due to chance (and thus not meaningful). In psychology, group differences are considered meaningful, or significant, if the odds that these differences occurred by chance alone are 5 percent or less. Stated another way, if we repeated this...
21.1K
01:09Decision Making: P-value Method
6.8K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim is also stated. These statements can act as null and alternative hypotheses: a null hypothesis would be a neutral statement while the alternative hypothesis can...
6.8K
01:14Errors In Hypothesis Tests
5.8K
When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.
5.8K
01:04Significance Testing: Overview
11.5K
Significance testing is a set of statistical methods used to test whether a claim about a parameter is valid. In analytical chemistry, significance testing is used primarily to determine whether the difference between two values comes from determinate or random errors. The effect of a particular change in the measurement protocol, analyst, or sample itself can cause a deviation from the expected result. In the case of a suspected deviation/outlier, we need to be able to confirm mathematically...
11.5K
01:10P-value
8.6K
P-value is one of the most crucial concepts in statistics.
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...
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...
8.6K
01:21Friedman Two-way Analysis of Variance by Ranks
483
Friedman's Two-Way Analysis of Variance by Ranks is a nonparametric test designed to identify differences across multiple test attempts when traditional assumptions of normality and equal variances do not apply. Unlike conventional ANOVA, which requires normally distributed data with equal variances, Friedman's test is ideal for ordinal or non-normally distributed data, making it particularly useful for analyzing dependent samples, such as matched subjects over time or repeated measures...
483