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  2. One-way analysis of variance - Wikipedia

    en.wikipedia.org/wiki/One-way_analysis_of_variance

    Since F=9.3 > 3.68, the results are significant at the 5% significance level. One would not accept the null hypothesis, concluding that there is strong evidence that the expected values in the three groups differ. The p-value for this test is 0.002. After performing the F-test, it is common to carry out some "post-hoc" analysis of the group ...

  3. Analysis of variance - Wikipedia

    en.wikipedia.org/wiki/Analysis_of_variance

    The ANOVA F-test is known to be nearly optimal in the sense of minimizing false negative errors for a fixed rate of false positive errors (i.e. maximizing power for a fixed significance level). For example, to test the hypothesis that various medical treatments have exactly the same effect, the F-test's p-values closely approximate the ...

  4. p-value - Wikipedia

    en.wikipedia.org/wiki/P-value

    p. -value. In null-hypothesis significance testing, the -value[note 1] is the probability of obtaining test results at least as extreme as the result actually observed, under the assumption that the null hypothesis is correct. [2][3] A very small p -value means that such an extreme observed outcome would be very unlikely under the null hypothesis.

  5. Kruskal–Wallis test - Wikipedia

    en.wikipedia.org/wiki/Kruskal–Wallis_test

    The Kruskal–Wallis test' by ranks, Kruskal–Wallis test (named after William Kruskal and W. Allen Wallis), or one-way ANOVA on ranks is a non-parametric statistical test for testing whether samples originate from the same distribution. [ 1 ][ 2 ][ 3 ] It is used for comparing two or more independent samples of equal or different sample sizes.

  6. Omnibus test - Wikipedia

    en.wikipedia.org/wiki/Omnibus_test

    The omnibus F ANOVA test results above indicate significant differences between the days time-wait (P-Value =0.000 < 0.05, α =0.05). The other omnibus tested was the assumption of Equality of Variances, tested by the Levene F test:

  7. Statistical hypothesis test - Wikipedia

    en.wikipedia.org/wiki/Statistical_hypothesis_test

    A statistical hypothesis test typically involves a calculation of a test statistic. Then a decision is made, either by comparing the test statistic to a critical value or equivalently by evaluating a p -value computed from the test statistic. Roughly 100 specialized statistical tests have been defined. [ 1 ][ 2 ]

  8. Tukey's range test - Wikipedia

    en.wikipedia.org/wiki/Tukey's_range_test

    The value q s is the sample's test statistic. (The notation | x | means the absolute value of x; the magnitude of x with the sign set to +, regardless of the original sign of x.) This q s test statistic can then be compared to a q value for the chosen significance level α from a table of the studentized range distribution.

  9. Statistical significance - Wikipedia

    en.wikipedia.org/wiki/Statistical_significance

    Statistical significance. In statistical hypothesis testing, [1][2] a result has statistical significance when a result at least as "extreme" would be very infrequent if the null hypothesis were true. [3] More precisely, a study's defined significance level, denoted by , is the probability of the study rejecting the null hypothesis, given that ...