1. Fisher's significance test: A gentle introduction.
- Author
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Stang, Andreas and Kowall, Bernd
- Subjects
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SCIENTIFIC errors , *FISHER exact test , *RESEARCH methodology , *PROBABILITY theory , *STATISTICAL sampling , *STATISTICAL hypothesis testing , *T-test (Statistics) , *STATISTICAL models , *DESCRIPTIVE statistics , *SAMPLING errors , *NULL hypothesis - Abstract
The p-value is often misunderstood and, for example, misinterpreted as a probability for the correctness of the null hypothesis. The aim of this article is to first explain the definition of the p-value. Determining the p-value requires knowledge of a probability function. Howan appropriate statistical model is selected and how the p-value is determined usingthis model, the null hypothesis and the empirical data is explained using the t-distribution. When interpreting the p-value obtained in this way, two incompatible statistical schools of thought are confronted: the orthodox Neyman-Pearson hypothesis test, which amounts to a decision between the null hypothesis and a complementary alternative hypothesis, and Fisher's significance test, in which no alternative hypothesis is formulated and in which the smaller the p-value, the greater the evidence against the null hypothesis. The amount ends with some critical remarks about the handling of p-values. [ABSTRACT FROM AUTHOR]
- Published
- 2020
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