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  2. Equivalence test - Wikipedia

    en.wikipedia.org/wiki/Equivalence_test

    A very simple equivalence testing approach is the ‘two one-sided t-tests’ (TOST) procedure. [11] In the TOST procedure an upper (Δ U) and lower (–Δ L) equivalence bound is specified based on the smallest effect size of interest (e.g., a positive or negative difference of d = 0.3).

  3. One- and two-tailed tests - Wikipedia

    en.wikipedia.org/wiki/One-_and_two-tailed_tests

    A two-tailed test applied to the normal distribution. A one-tailed test, showing the p-value as the size of one tail. In statistical significance testing, a one-tailed test and a two-tailed test are alternative ways of computing the statistical significance of a parameter inferred from a data set, in terms of a test statistic. A two-tailed test ...

  4. Statistical hypothesis test - Wikipedia

    en.wikipedia.org/wiki/Statistical_hypothesis_test

    The original example is termed a one-sided or a one-tailed test while the generalization is termed a two-sided or two-tailed test. The statement also relies on the inference that the sampling was random.

  5. Dunnett's test - Wikipedia

    en.wikipedia.org/wiki/Dunnett's_test

    Dunnett's test's calculation is a procedure that is based on calculating confidence statements about the true or the expected values of the differences ¯ ¯, thus the differences between treatment groups' mean and control group's mean.

  6. Student's t-test - Wikipedia

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

    Student's t-test is a statistical test used to test whether the difference between the response of two groups is statistically significant or not. It is any statistical hypothesis test in which the test statistic follows a Student's t -distribution under the null hypothesis .

  7. Grubbs's test - Wikipedia

    en.wikipedia.org/wiki/Grubbs's_test

    Grubbs's test can also be defined as a one-sided test, replacing α/(2N) with α/N. To test whether the minimum value is an outlier, the test statistic is = ¯ with Y min denoting the minimum value. To test whether the maximum value is an outlier, the test statistic is

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  9. Jonckheere's trend test - Wikipedia

    en.wikipedia.org/wiki/Jonckheere's_Trend_Test

    For α = 0.05 (one-sided) the critical z value is 1.645, so again the result would be declared significant at this level. A similar test for trend within the context of repeated measures (within-participants) designs and based on Spearman's rank correlation coefficient was developed by Page. [6]