Fisher's significance test: A gentle introduction.
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 statisti...
| Publicado en: | GMS Medizinische Informatik, Biometrie und Epidemiologie Vol. 16; no. 1; pp. 1 - 16 |
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| Autores principales: | , |
| Formato: | equations & formulas tables/charts Journal Article |
| Publicado: |
German Medical Science Publishing House gGmbH
2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=145974848&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 145974848 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 18608779 780E jtl: GMS Medizinische Informatik, Biometrie und Epidemiologie issn: 18608779 maglogo: N pubinfo: dt: 2020 vid: 16 iid: 1 pid: 24764 pub: German Medical Science Publishing House gGmbH place: 40223 Duesseldorf, <Blank> artinfo: ui: 145974848 145974848 145974848 10.3205/mibe000206, 145974848 ppf: 1 ppct: 15 formats: tig: atl: Fisher's significance test: A gentle introduction. aug: au: Stang, Andreas Kowall, Bernd affil: Institute of Medical Informatics, Biometry and Epidemiology sug: subj: Fisher's Exact Test Significance Test P-Value Models, Statistical Null Hypothesis Descriptive Statistics Sampling Error Random Sample Random Error T-Tests ab: 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. pubtype: Academic Journal doctype: equations & formulas tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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