Egon Pearson

Egon Sharpe Pearson (1895–1980) was a British statistician who developed the Neyman–Pearson theory of hypothesis testing with Jerzy Neyman.

Hypothesis testing: A statistical procedure that uses sample data to evaluate a claim about a population. Pearson and Neyman formalized how tests should distinguish competing statistical claims.
Jerzy Neyman: A Polish-American mathematician and statistician who developed frequentist inference and hypothesis-testing theory. Neyman was Pearson's principal collaborator in formulating their theory of tests.
University College London: A public research university in London, founded in 1826. Pearson studied and later worked at the institution associated with his father's statistical department.
Neyman–Pearson theory: A framework for choosing statistical tests by controlling error probabilities and comparing their power under alternatives. This is the central body of theory Pearson developed with Neyman.
Fisherian significance testing: An approach that uses a test statistic and its null distribution to assess evidence against a null hypothesis. Fisher rejected the Neyman–Pearson emphasis on long-run error control and specified alternatives.
Null hypothesis: A statistical claim treated as the reference assumption in a hypothesis test. Their framework specifies how evidence is assessed against this reference claim.
Karl Pearson: A British statistician who founded the Department of Applied Statistics at University College London. Karl Pearson was Egon's father and predecessor in the same statistical institution.
Karl Pearson's Department of Applied Statistics: A research and teaching department founded by Karl Pearson at University College London. Egon Pearson succeeded his father in leading this department.
Frequentist statistics: An approach to statistical inference that interprets probability through long-run frequencies of outcomes. Their testing framework became a foundational part of frequentist inference.
P-value: The probability, under a specified null model, of obtaining a test statistic at least as extreme as the observed one. Fisher treated the p-value as a measure of evidence, unlike a fixed decision rule.

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