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Although the 30 samples were all simulated under the null, one of the resulting p-values is small enough to produce a false rejection at the typical level 0.05 in the absence of correction. Multiple comparisons arise when a statistical analysis involves multiple simultaneous statistical tests, each of which has a potential to produce a "discovery".
In Petrophysics a Klinkenberg correction is a procedure for calibration of permeability data obtained from a minipermeameter device. A more accurate correction factor can be obtained using Knudsen correction .
The Šidák correction is derived by assuming that the individual tests are independent. Let the significance threshold for each test be α 1 {\displaystyle \alpha _{1}} ; then the probability that at least one of the tests is significant under this threshold is (1 - the probability that none of them are significant).
The bias-corrected and accelerated ... but with a different formula (note the inversion of the left and right quantiles): ... for a sample size n; this ...
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.
The simplest version of the test statistic from this auxiliary regression is TR 2, where T is the sample size and R 2 is the coefficient of determination. Under the null hypothesis of no autocorrelation, this statistic is asymptotically distributed as χ 2 {\displaystyle \chi ^{2}} with k degrees of freedom.
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 ...
This is because () is the smallest in each one of the intersection sub-families and the size of the sub-families is at most , such that the Bonferroni threshold larger than /. The same rationale applies for H ( 2 ) {\displaystyle H_{(2)}} .