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Deciding the statistical significance of nonparametric tests with large
Deciding the statistical significance of nonparametric tests with large

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... Type II error greater. ...
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... least think if independence is reasonable. Randomization Condition: The data comes from a random sample or randomized experiment. This helps with independence. 10% Condition: When sample is drawn without replacement, the sample should be no more than 10% of the population. Nearly Normal Condition: T ...
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Tests with two+ groups - University of California, Riverside

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... • If there is a reason to believe that this assumption is not valid in any given problem, then one has to transform the data or to rely on nonparametric methods. ...
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Resampling (statistics)

In statistics, resampling is any of a variety of methods for doing one of the following: Estimating the precision of sample statistics (medians, variances, percentiles) by using subsets of available data (jackknifing) or drawing randomly with replacement from a set of data points (bootstrapping) Exchanging labels on data points when performing significance tests (permutation tests, also called exact tests, randomization tests, or re-randomization tests) Validating models by using random subsets (bootstrapping, cross validation)Common resampling techniques include bootstrapping, jackknifing and permutation tests.
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