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Related Concept Videos

P-value01:10

P-value

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 unlikely...
Decision Making: P-value Method01:09

Decision Making: P-value Method

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 have a...
Hypothesis: Accept or Fail to Reject?01:17

Hypothesis: Accept or Fail to Reject?

The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null hypothesis and 'fail to...
Testing a Claim about Population Proportion01:24

Testing a Claim about Population Proportion

A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
The first method uses normal distribution as an approximation to the binomial distribution. The requirements are as follows: sample size is large...
Errors In Hypothesis Tests01:14

Errors In Hypothesis Tests

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.
Statistical Significance01:37

Statistical Significance

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...

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Related Experiment Video

Updated: Jun 6, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

Hail the impossible: p-values, evidence, and likelihood.

Tobias Johansson1

  • 1Kristianstad University, Kristianstad, Sweden. Tobias.Johansson@hkr.se

Scandinavian Journal of Psychology
|November 17, 2010
PubMed
Summary
This summary is machine-generated.

P-values are commonly misused in psychology as evidence against the null hypothesis. This study highlights four major problems with this Fisherian interpretation and recommends the likelihood ratio for a more accurate representation of statistical evidence.

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Area of Science:

  • Psychological research methodology
  • Statistical inference

Background:

  • P-values are standard in psychological research and teaching.
  • P-values are often used to measure statistical evidence against the null hypothesis (Fisherian interpretation).
  • This interpretation overlooks fundamental statistical and conceptual problems.

Purpose of the Study:

  • To identify and explain the problems with using p-values as a measure of statistical evidence in psychology.
  • To propose an alternative method for representing statistical evidence.

Main Methods:

  • Conceptual analysis of p-value interpretations (Fisherian vs. Neyman-Pearson).
  • Identification of four key problems with the Fisherian use of p-values.
  • Comparison with the likelihood ratio as a measure of evidence.

Main Results:

  • P-values are uniformly distributed under the null hypothesis, incapable of supporting it.
  • P-values are conditioned solely on the null hypothesis, not suitable for relative evidence quantification.
  • P-values represent the probability of obtaining evidence, not the strength of evidence.
  • P-values depend on unobserved data and subjective intentions, complicating evidential interpretation.

Conclusions:

  • Using p-values as a measure of statistical evidence in the Fisherian sense is statistically and conceptually problematic.
  • The Neyman-Pearson interpretation of p-values does not concern evidence.
  • The likelihood ratio is recommended as a superior tool for psychologists to represent statistical evidence relative to two hypotheses.