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  2. Sample size determination - Wikipedia

    en.wikipedia.org/wiki/Sample_size_determination

    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 based on the cost, time, or convenience of collecting the data, and the need for it to offer sufficient statistical power .

  3. 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 [20] Select the table corresponding to the selected α; Locate the row corresponding to the desired power; Locate the column corresponding ...

  4. Freedman–Diaconis rule - Wikipedia

    en.wikipedia.org/wiki/Freedman–Diaconis_rule

    where ⁡ is the interquartile range of the data and is the number of observations in the sample . In fact if the normal density is used the factor 2 in front comes out to be ∼ 2.59 {\displaystyle \sim 2.59} , [ 4 ] but 2 is the factor recommended by Freedman and Diaconis.

  5. Sampling fraction - Wikipedia

    en.wikipedia.org/wiki/Sampling_fraction

    In sampling theory, the sampling fraction is the ratio of sample size to population size or, in the context of stratified sampling, the ratio of the sample size to the size of the stratum. [1] The formula for the sampling fraction is =, where n is the sample size and N is the population size. A sampling fraction value close to 1 will occur if ...

  6. 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.

  7. Bessel's correction - Wikipedia

    en.wikipedia.org/wiki/Bessel's_correction

    Generally Bessel's correction is an approach to reduce the bias due to finite sample size. Such finite-sample bias correction is also needed for other estimates like skew and kurtosis, but in these the inaccuracies are often significantly larger. To fully remove such bias it is necessary to do a more complex multi-parameter estimation.

  8. Shrinkage (statistics) - Wikipedia

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

    An example arises in the estimation of the population variance by sample variance. For a sample size of n , the use of a divisor n −1 in the usual formula ( Bessel's correction ) gives an unbiased estimator, while other divisors have lower MSE, at the expense of bias.

  9. Šidák correction - Wikipedia

    en.wikipedia.org/wiki/Šidák_correction

    It is credited to a 1967 paper [1] by the statistician and probabilist Zbyněk Šidák. [2] The Šidák method can be used to adjust alpha levels, p-values, or confidence intervals. Usage