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  2. Normality test - Wikipedia

    en.wikipedia.org/wiki/Normality_test

    Simple back-of-the-envelope test takes the sample maximum and minimum and computes their z-score, or more properly t-statistic (number of sample standard deviations that a sample is above or below the sample mean), and compares it to the 68–95–99.7 rule: if one has a 3σ event (properly, a 3s event) and substantially fewer than 300 samples, or a 4s event and substantially fewer than 15,000 ...

  3. List of statistical tests - Wikipedia

    en.wikipedia.org/wiki/List_of_statistical_tests

    The choice between these two groups needs to be justified. Parametric tests assume that the data follow a particular distribution, typically a normal distribution, while non-parametric tests make no assumptions about the distribution. [7] Non-parametric tests have the advantage of being more resistant to misbehaviour of the data, such as ...

  4. Shapiro–Wilk test - Wikipedia

    en.wikipedia.org/wiki/Shapiro–Wilk_test

    The Shapiro–Wilk test tests the null hypothesis that a sample x 1, ..., x n came from a normally distributed population. The test statistic is = (= ()) = (¯), where with parentheses enclosing the subscript index i is the ith order statistic, i.e., the ith-smallest number in the sample (not to be confused with ).

  5. Newman–Keuls method - Wikipedia

    en.wikipedia.org/wiki/Newman–Keuls_method

    The test is quite robust to violations of normality. Violating homogeneity of variance can be more problematic than in the two-sample case since the MSE is based on data from all groups. The assumption of independence of observations is important and should not be violated.

  6. Goodness of fit - Wikipedia

    en.wikipedia.org/wiki/Goodness_of_fit

    In assessing whether a given distribution is suited to a data-set, the following tests and their underlying measures of fit can be used: Bayesian information criterion; Kolmogorov–Smirnov test; Cramér–von Mises criterion; Anderson–Darling test; Berk-Jones tests [1] [2] Shapiro–Wilk test; Chi-squared test; Akaike information criterion ...

  7. Kurtosis - Wikipedia

    en.wikipedia.org/wiki/Kurtosis

    D'Agostino's K-squared test is a goodness-of-fit normality test based on a combination of the sample skewness and sample kurtosis, as is the Jarque–Bera test for normality. For non-normal samples, the variance of the sample variance depends on the kurtosis; for details, please see variance .

  8. Eccentricity (behavior) - Wikipedia

    en.wikipedia.org/wiki/Eccentricity_(behavior)

    Eccentricity (also called quirkiness) is an unusual or odd behavior on the part of an individual. This behavior would typically be perceived as unusual or unnecessary, without being demonstrably maladaptive .

  9. Category:Normality tests - Wikipedia

    en.wikipedia.org/wiki/Category:Normality_tests

    It should only contain pages that are Normality tests or lists of Normality tests, as well as subcategories containing those things (themselves set categories). Topics about Normality tests in general should be placed in relevant topic categories .