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  2. Two-sample hypothesis testing - Wikipedia

    en.wikipedia.org/wiki/Two-sample_hypothesis_testing

    In statistical hypothesis testing, a two-sample test is a test performed on the data of two random samples, each independently obtained from a different given population. The purpose of the test is to determine whether the difference between these two populations is statistically significant .

  3. Template : Classification of multiple hypothesis tests

    en.wikipedia.org/wiki/Template:Classification_of...

    The following table defines the possible outcomes when testing multiple null hypotheses. Suppose we have a number m of null hypotheses, denoted by: H 1, H 2, ..., H m. Using a statistical test, we reject the null hypothesis if the test is declared significant. We do not reject the null hypothesis if the test is non-significant.

  4. Permutation test - Wikipedia

    en.wikipedia.org/wiki/Permutation_test

    A permutation test (also called re-randomization test or shuffle test) is an exact statistical hypothesis test making use of the proof by contradiction. A permutation test involves two or more samples. The null hypothesis is that all samples come from the same distribution : =.

  5. Welch's t-test - Wikipedia

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

    In statistics, Welch's t-test, or unequal variances t-test, is a two-sample location test which is used to test the (null) hypothesis that two populations have equal means. It is named for its creator, Bernard Lewis Welch , and is an adaptation of Student's t -test , [ 1 ] and is more reliable when the two samples have unequal variances and ...

  6. Test statistic - Wikipedia

    en.wikipedia.org/wiki/Test_statistic

    Test statistic is a quantity derived from the sample for statistical hypothesis testing. [1] A hypothesis test is typically specified in terms of a test statistic, considered as a numerical summary of a data-set that reduces the data to one value that can be used to perform the hypothesis test.

  7. Type I and type II errors - Wikipedia

    en.wikipedia.org/wiki/Type_I_and_type_II_errors

    The consistent application by statisticians of Neyman and Pearson's convention of representing "the hypothesis to be tested" (or "the hypothesis to be nullified") with the expression H 0 has led to circumstances where many understand the term "the null hypothesis" as meaning "the nil hypothesis" – a statement that the results in question have ...

  8. Template:List of statistics symbols - Wikipedia

    en.wikipedia.org/wiki/Template:List_of...

    In general, the subscript 0 indicates a value taken from the null hypothesis, H 0, which should be used as much as possible in constructing its test statistic. ... Definitions of other symbols: Definitions of other symbols:

  9. Sample size determination - Wikipedia

    en.wikipedia.org/wiki/Sample_size_determination

    is a decision rule which satisfies (2). (This is a 1-tailed test.) In such a scenario, achieving this with a probability of at least 1−β when the alternative hypothesis H a is true becomes imperative. Here, the sample average originates from a Normal distribution with a mean of μ *. Thus, the requirement is expressed as: