Related Experiment Video
Updated: Jan 2, 2026

Mapping Dysfunctional Protein-Protein Interactions in Disease
Published on: October 24, 2025
Misinterpreting p: The discrepancy between p values and the probability the null hypothesis is true, the influence of
1Department of Psychology.
Abstract:
The p value is still misinterpreted as the probability that the null hypothesis is true. Even psychologists who correctly understand that p values do not provide this probability may not realize the degree to which p values differ from the probability that the null hypothesis is true. Importantly, previous research on this topic has not addressed the influence of multiple testing, often a reality in psychological studies, and has not extensively considered the influence of different prior probabilities favoring the null and alternative hypotheses. Simulation studies are presented that emphasize the magnitude by which p values are distinct from the posterior probability that the null hypothesis is true, under an extensive set of conditions including multiple testing. Particular emphasis is placed on p values just under .05, given the prevalence of these p values in the published literature, though p values in other intervals are also assessed. In diverse conditions, results indicate that posterior probabilities favoring the null hypothesis are often far removed from .05, and this pattern quickly gets much worse when multiple testing is conducted. Rather than simply telling researchers that p values do not reflect the probability favoring the null hypothesis, as has been done previously, the results presented here allow psychologists to see the evidence provided by various p values. These results have particularly topical implications for the replication crisis, for how much weight should be placed on a single study, and for how the term statistical significance should be interpreted, particularly in conditions typical in psychological research. (PsycInfo Database Record (c) 2020 APA, all rights reserved).
More Related Videos
Related Concept Videos
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%...
Errors In Hypothesis Tests
Decision Making: P-value Method
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...
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...
Significance Testing: Overview
Statistical Hypothesis Testing
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

