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For the sake of curiosity, other major statistical hypotheses are considered as well.
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Due to the huge number of genotyped SNPs, genome-wide scans are faced with a major statistical problem even when a single underlying biological hypothesis is being tested.
Such hypotheses represent a kind of disjunction of simple statistical hypotheses.
Bayesian inference always starts from a statistical model, i.e., a set of statistical hypotheses.
Likelihoodists contrast simple statistical hypotheses with composite statistical hypotheses, which only entail vague, or imprecise, or directional claims about the statistical probabilities of evidential events.
can be interpreted as the expected discrimination information between the null and alternative statistical hypotheses, for discriminating in favor for a hypothesis, against hypothesis, when hypothesis is true.
Model identification and discrimination are two major statistical challenges.
Recall that a prior probability distribution over statistical hypotheses expresses our uncertain opinion on which of the hypotheses is right.
Statistical methods provide the mathematical and conceptual means to evaluate statistical hypotheses in the light of a sample.
A significance level of P < 0.05 was chosen in all statistical hypotheses, and all statistical tests were two-sided.
Most importantly, all statistical procedures rely on the assumption of a statistical model, here referring to any restricted set of statistical hypotheses.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com