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  2. p-value - Wikipedia

    en.wikipedia.org/wiki/P-value

    In a significance test, the null hypothesis is rejected if the p-value is less than or equal to a predefined threshold value , which is referred to as the alpha level or significance level. α {\displaystyle \alpha } is not derived from the data, but rather is set by the researcher before examining the data.

  3. Goodness of fit - Wikipedia

    en.wikipedia.org/wiki/Goodness_of_fit

    where and are the same as for the chi-square test, denotes the natural logarithm, and the sum is taken over all non-empty bins. Furthermore, the total observed count should be equal to the total expected count: ∑ i O i = ∑ i E i = N {\displaystyle \sum _{i}O_{i}=\sum _{i}E_{i}=N} where N {\textstyle N} is the total number of observations.

  4. Statistical significance - Wikipedia

    en.wikipedia.org/wiki/Statistical_significance

    A two-tailed test may still be used but it will be less powerful than a one-tailed test, because the rejection region for a one-tailed test is concentrated on one end of the null distribution and is twice the size (5% vs. 2.5%) of each rejection region for a two-tailed test.

  5. Fisher's exact test - Wikipedia

    en.wikipedia.org/wiki/Fisher's_exact_test

    An alternative exact test, Barnard's exact test, has been developed and proponents [22] of it suggest that this method is more powerful, particularly in 2×2 tables. [23] Furthermore, Boschloo's test is an exact test that is uniformly more powerful than Fisher's exact test by construction. [24]

  6. Šidák correction - Wikipedia

    en.wikipedia.org/wiki/Šidák_correction

    It is less stringent than the Bonferroni correction, but only slightly. For example, for α {\displaystyle \alpha } = 0.05 and m = 10, the Bonferroni-adjusted level is 0.005 and the Šidák-adjusted level is approximately 0.005116.

  7. Anderson–Darling test - Wikipedia

    en.wikipedia.org/wiki/Anderson–Darling_test

    The Anderson–Darling test is a statistical test of whether a given sample of data is drawn from a given probability distribution. In its basic form, the test assumes that there are no parameters to be estimated in the distribution being tested, in which case the test and its set of critical values is distribution-free.

  8. Tukey's range test - Wikipedia

    en.wikipedia.org/wiki/Tukey's_range_test

    Since the null hypothesis for Tukey's test states that all means being compared are from the same population (i.e. μ 1 = μ 2 = μ 3 = ... = μ k), the means should be normally distributed (according to the central limit theorem) with the same model standard deviation σ, estimated by the merged standard error, , for all the samples; its ...

  9. Student's t-distribution - Wikipedia

    en.wikipedia.org/wiki/Student's_t-distribution

    For the statistic t, with ν degrees of freedom, A(t | ν) is the probability that t would be less than the observed value if the two means were the same (provided that the smaller mean is subtracted from the larger, so that t ≥ 0). It can be easily calculated from the cumulative distribution function F ν (t) of the t distribution:

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