Process / pipelinehypothesis-testing

P-Value and Statistical Significance

The p-value is the probability of observing data as extreme as or more extreme than what was actually observed, assuming the null hypothesis is true. Introduced by Ronald Fisher in 1925, it is the foundation of frequentist hypothesis testing. Statistical significance is declared when the p-value falls below a pre-specified threshold (alpha level, typically 0.05).

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Sources

  1. Fisher, R. A. (1925). Statistical Methods for Research Workers. Oliver and Boyd. link
  2. Neyman, J., & Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society, 231, 289–337. DOI: 10.1098/rsta.1933.0009
  3. Wasserstein, R. L., & Lazar, N. A. (2016). The ASA Statement on p-Values: Context, Process, and Purpose. The American Statistician, 70(2), 129–133. DOI: 10.1080/00031305.2016.1154108

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Referenced by

ScholarGateP-Value and Statistical Significance (P-Value and the Concept of Statistical Significance in Hypothesis Testing). Retrieved 2026-06-04 from https://scholargate.app/en/research-statistics/p-value-significance