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  2. Sample size determination - Wikipedia

    en.wikipedia.org/wiki/Sample_size_determination

    The table shown on the right can be used in a two-sample t-test to estimate the sample sizes of an experimental group and a control group that are of equal size, that is, the total number of individuals in the trial is twice that of the number given, and the desired significance level is 0.05. [4]

  3. Design effect - Wikipedia

    en.wikipedia.org/wiki/Design_effect

    If the sample size is 1,000, then the effective sample size will be 500. It means that the variance of the weighted mean based on 1,000 samples will be the same as that of a simple mean based on 500 samples obtained using a simple random sample.

  4. 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 .

  5. Size (statistics) - Wikipedia

    en.wikipedia.org/wiki/Size_(statistics)

    In the case of a composite null hypothesis, the size is the supremum over all data generating processes that satisfy the null hypotheses. [1]

  6. Power (statistics) - Wikipedia

    en.wikipedia.org/wiki/Power_(statistics)

    Lehr's [3] [4] (rough) rule of thumb says that the sample size (for each group) for the common case of a two-sided two-sample t-test with power 80% (=) and significance level = should be: , where is an estimate of the population variance and = the to-be-detected difference in the mean values of both samples.

  7. Efficiency (statistics) - Wikipedia

    en.wikipedia.org/wiki/Efficiency_(statistics)

    The efficiencies and the relative efficiency of two procedures theoretically depend on the sample size available for the given procedure, but it is often possible to use the asymptotic relative efficiency (defined as the limit of the relative efficiencies as the sample size grows) as the principal comparison measure.

  8. Replication (statistics) - Wikipedia

    en.wikipedia.org/wiki/Replication_(statistics)

    In engineering, science, and statistics, replication is the process of repeating a study or experiment under the same or similar conditions. It is a crucial step to test the original claim and confirm or reject the accuracy of results as well as for identifying and correcting the flaws in the original experiment. [1]

  9. Effect size - Wikipedia

    en.wikipedia.org/wiki/Effect_size

    In statistics, an effect size is a value measuring the strength of the relationship between two variables in a population, or a sample-based estimate of that quantity. It can refer to the value of a statistic calculated from a sample of data, the value of one parameter for a hypothetical population, or to the equation that operationalizes how statistics or parameters lead to the effect size ...