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.
2018
Istituto Applicazioni del Calcolo ''Mauro Picone''
9780128114148
hypothesis test
bioinformatics
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/352811
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