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  2. Sample size determination - Wikipedia

    en.wikipedia.org/wiki/Sample_size_determination

    Sample size determination or estimation is the act of choosing the number of observations or replicates to include in a statistical sample. The sample size is an important feature of any empirical study in which the goal is to make inferences about a population from a sample.

  3. Partial least squares path modeling - Wikipedia

    en.wikipedia.org/wiki/Partial_least_squares_path...

    A recent study suggests that this claim is generally unjustified, and proposes two methods for minimum sample size estimation in PLS-PM. [ 13 ] [ 14 ] Another point of contention is the ad hoc way in which PLS-PM has been developed and the lack of analytic proofs to support its main feature: the sampling distribution of PLS-PM weights.

  4. 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 ...

  5. Cohen's h - Wikipedia

    en.wikipedia.org/wiki/Cohen's_h

    It can be used in calculating the sample size for a future study. When measuring differences between proportions, Cohen's h can be used in conjunction with hypothesis testing . A " statistically significant " difference between two proportions is understood to mean that, given the data, it is likely that there is a difference in the population ...

  6. G*Power - Wikipedia

    en.wikipedia.org/wiki/G*Power

    A priori analyses are one of the most commonly used analyses in research and calculate the needed sample size in order to achieve a sufficient power level and requires inputted values for alpha and effect size. Compromise analyses find implied power based on the beta/alpha ratio, or q, and inputted values for effect size and sample size.

  7. Bootstrapping (statistics) - Wikipedia

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

    Usually the sample drawn has the same sample size as the original data. Then the estimate of original function F can be written as F ^ = F θ ^ {\displaystyle {\hat {F}}=F_{\hat {\theta }}} . This sampling process is repeated many times as for other bootstrap methods.

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  9. Ratio estimator - Wikipedia

    en.wikipedia.org/wiki/Ratio_estimator

    where n is the size of the sample and the r i are estimated with the omission of one pair of variates at a time. [10] An alternative method is to divide the sample into g groups each of size p with n = pg. [11] Let r i be the estimate of the i th group. Then the estimator