P value
P value
The P value (pronounced as "pee value") is a statistical term that helps to determine the significance of results from a scientific study or experiment.
Pronunciation
P value: /piː ˈvæl.juː/
Etymology
The term "P value" was first used by British statistician Ronald A. Fisher in the 1920s. The "P" stands for "probability".
Definition
In statistics, the P value is the probability of obtaining test results at least as extreme as the results actually observed, under the assumption that the null hypothesis is correct. A smaller P value suggests that the observed data is less likely to have occurred by chance, thus providing stronger evidence against the null hypothesis.
Related Terms
- Null Hypothesis: A general statement or default position that there is no relationship between two measured phenomena.
- Alternative Hypothesis: The hypothesis that is contrary to the null hypothesis. It is usually taken to be that the observations are the result of a real effect.
- Statistical Significance: A statistical statement of how likely it is that a given set of results could have occurred by chance.
- Type I Error: The incorrect rejection of a true null hypothesis.
- Type II Error: The failure to reject a false null hypothesis.
Usage
The P value is widely used in scientific research to test hypotheses and validate experimental results. It is a key component in many statistical tests, including the t-test, ANOVA, and chi-square test.
Criticisms
Despite its widespread use, the P value has been criticized for its potential to mislead when used improperly. Misinterpretations and misuse of P values can lead to false conclusions and scientific irreproducibility.
External links
- Medical encyclopedia article on P value
- Wikipedia's article - P value
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