This chapter presents the most common and useful tests of hypothesis for bioinformatics applications. The hypothesis tests divide essentially into two categories: parametric and nonparametric. At the first category belong those tests based on the assumption of knowing the distribution of the sampling population(s) and inference is drawn on one or more unknown parameter(s); at the second category belong those tests that are "distribution-free" which generally have much less assumptions. For each test, we will present the mathematical hypothesis under which it is applicable and the statistics used to apply it.
Statistical inference tecniques
D De Canditiis
2018
Abstract
This chapter presents the most common and useful tests of hypothesis for bioinformatics applications. The hypothesis tests divide essentially into two categories: parametric and nonparametric. At the first category belong those tests based on the assumption of knowing the distribution of the sampling population(s) and inference is drawn on one or more unknown parameter(s); at the second category belong those tests that are "distribution-free" which generally have much less assumptions. For each test, we will present the mathematical hypothesis under which it is applicable and the statistics used to apply it.File in questo prodotto:
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