Normality Tests - short version

Tests of whether a set of data is distributed in a way that is consistent with a normal distribution.

Normality Tests - long version

normality tests are used to determine whether a data set is well-modeled by a normal distribution or not, or to compute how likely an underlying random variable is to be normally distributed. More precisely, they are a form of model selection, and can be interpreted several ways, depending on one's interpretations of probability:

* In descriptive statistics terms, one measures a goodness of fit of a normal model to the data – if the fit is poor then the data is not well modeled in that respect by a normal distribution, without making a judgment on any underlying variable.

* In frequentist statistics statistical hypothesis testing, data are tested against the null hypothesis that it is normally distributed.

* In Bayesian statistics, one does not "test normality" per se, but rather computes the likelihood that the data comes from a normal distribution with given parameters μ,σ (for all μ,σ), and compares that with the likelihood that the data comes from other distributions under consideration, most simply using Bayes factors (giving the relatively likelihood of seeing the data given different models), or more finely taking a prior distribution on possible models and parameters and computing a posterior distribution given the computed likelihoods.

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