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  2. Cohen's h - Wikipedia

    en.wikipedia.org/wiki/Cohen's_h

    In statistics, Cohen's h, popularized by Jacob Cohen, is a measure of distance between two proportions or probabilities. Cohen's h has several related uses: It can be used to describe the difference between two proportions as "small", "medium", or "large". It can be used to determine if the difference between two proportions is "meaningful".

  3. 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]

  4. Beta distribution - Wikipedia

    en.wikipedia.org/wiki/Beta_distribution

    For sample size much larger than 2, the difference between these two priors becomes negligible. (See section Bayesian inference for further details.) ν = α + β is referred to as the "sample size" of a beta distribution, but one should remember that it is, strictly speaking, the "sample size" of a binomial likelihood function only when using ...

  5. Tukey's range test - Wikipedia

    en.wikipedia.org/wiki/Tukey's_range_test

    Suppose that we take a sample of size n from each of k populations with the same normal distribution N(μ, σ 2) and suppose that ¯ is the smallest of these sample means and ¯ is the largest of these sample means, and suppose S 2 is the pooled sample variance from these samples. Then the following random variable has a Studentized range ...

  6. Ratio estimator - Wikipedia

    en.wikipedia.org/wiki/Ratio_estimator

    where N is the population size, n is the sample size, m x is the mean of the x variate and s x 2 and s y 2 are the sample variances of the x and y variates respectively. These versions differ only in the factor in the denominator (N - 1). For a large N the difference is negligible. If x and y are unitless counts with Poisson distribution a ...

  7. Statistical hypothesis test - Wikipedia

    en.wikipedia.org/wiki/Statistical_hypothesis_test

    Set up a statistical null hypothesis. The null need not be a nil hypothesis (i.e., zero difference). Set up two statistical hypotheses, H1 and H2, and decide about α, β, and sample size before the experiment, based on subjective cost-benefit considerations. These define a rejection region for each hypothesis. 2

  8. Hodges–Lehmann estimator - Wikipedia

    en.wikipedia.org/wiki/Hodges–Lehmann_estimator

    The two-sample Hodges–Lehmann statistic is an estimate of a location-shift type difference between two populations. For two sets of data with m and n observations, the set of two-element sets made of them is their Cartesian product, which contains m × n pairs of points (one from each set); each such pair defines one difference of values.

  9. Design effect - Wikipedia

    en.wikipedia.org/wiki/Design_effect

    For example, let the design effect, for estimating the population mean based on some sampling design, be 2. 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 ...