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Fisher's significance test: A gentle introduction.
- Source :
- GMS Medizinische Informatik, Biometrie und Epidemiologie; 2020, Vol. 16 Issue 1, p1-15, 15p
- Publication Year :
- 2020
-
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]
Details
- Language :
- English
- ISSN :
- 18608779
- Volume :
- 16
- Issue :
- 1
- Database :
- Complementary Index
- Journal :
- GMS Medizinische Informatik, Biometrie und Epidemiologie
- Publication Type :
- Academic Journal
- Accession number :
- 145974848
- Full Text :
- https://doi.org/10.3205/mibe000206,