P value

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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.

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