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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. In practice, the sample size used in a study is usually determined ...

  3. Fisher's exact test - Wikipedia

    en.wikipedia.org/wiki/Fisher's_exact_test

    Fisher's exact test. Fisher's exact test is a statistical significance test used in the analysis of contingency tables. [ 1][ 2][ 3] Although in practice it is employed when sample sizes are small, it is valid for all sample sizes. It is named after its inventor, Ronald Fisher, and is one of a class of exact tests, so called because the ...

  4. Sampling (statistics) - Wikipedia

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

    Formulas, tables, and power function charts are well known approaches to determine sample size. Steps for using sample size tables: Postulate the effect size of interest, α, and β. Check sample size table [18] Select the table corresponding to the selected α; Locate the row corresponding to the desired power

  5. Design effect - Wikipedia

    en.wikipedia.org/wiki/Design_effect

    In survey methodology, the design effect (generally denoted as , , or ) is a measure of the expected impact of a sampling design on the variance of an estimator for some parameter of a population. It is calculated as the ratio of the variance of an estimator based on a sample from an (often) complex sampling design, to the variance of an ...

  6. Power (statistics) - Wikipedia

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

    According to this formula, the power increases with the values of the effect size and the sample size n, and reduces with increasing variability . In the trivial case of zero effect size, power is at a minimum ( infimum ) and equal to the significance level of the test α , {\displaystyle \alpha \,,} in this example 0.05.

  7. Student's t-test - Wikipedia

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

    However, the sample size required for the sample means to converge to normality depends on the skewness of the distribution of the original data. The sample can vary from 30 to 100 or higher values depending on the skewness. [23] [24] F For non-normal data, the distribution of the sample variance may deviate substantially from a χ 2 distribution.

  8. Standard error - Wikipedia

    en.wikipedia.org/wiki/Standard_error

    This approximate formula is for moderate to large sample sizes; the reference gives the exact formulas for any sample size, and can be applied to heavily autocorrelated time series like Wall Street stock quotes. Moreover, this formula works for positive and negative ρ alike. [12] See also unbiased estimation of standard deviation for more ...

  9. Systematic sampling - Wikipedia

    en.wikipedia.org/wiki/Systematic_sampling

    Systematic sampling. In survey methodology, one-dimensional systematic sampling is a statistical method involving the selection of elements from an ordered sampling frame. The most common form of systematic sampling is an equiprobability method. [ 1] This applies in particular when the sampled units are individuals, households or corporations.