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In typical use, it is a function of the test used (including the desired level of statistical significance), the assumed distribution of the test (for example, the degree of variability, and sample size), and the effect size of interest. High statistical power is related to low variability, large sample sizes, large effects being looked for ...
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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]
However, as the example below shows, the binomial test is not restricted to this case. When there are more than two categories, and an exact test is required, the multinomial test, based on the multinomial distribution, must be used instead of the binomial test. [1] Most common measures of effect size for Binomial tests are Cohen's h or Cohen's g.
The RMSD serves to aggregate the magnitudes of the errors in predictions for various data points into a single measure of predictive power. RMSD is a measure of accuracy, to compare forecasting errors of different models for a particular dataset and not between datasets, as it is scale-dependent. [1]
The cost of this protection against type I errors is an increased risk of failing to reject one or more false null hypotheses (i.e., of committing one or more type II errors). The Holm–Bonferroni method also controls the FWER at , but with
For example, if two thirds of the sample was used for the first measurement and one third for the second and final measurement, then one might weight the first measurement twice that of the second.
This system detects all single-digit errors and around 90% [citation needed] of transposition errors. 1, 3, 7, and 9 are used because they are coprime with 10, so changing any digit changes the check digit; using a coefficient that is divisible by 2 or 5 would lose information (because 5×0 = 5×2 = 5×4 = 5×6 = 5×8 = 0 modulo 10) and thus ...