Comparison of the Fisher\u27s Method of Randomization with Other Tests Based on Ranks and the F-Test

Classical statistical inference methods (parametric methods) have a common denominator, i.e. a population para meter (μ, o, n) about which we wish to draw inferences from a random sample. R) are selected. Point estimators of the parameters (X, S, Their sampling distribution is used to construct hypothesis testing decision rules or, confidence interval formulas. This is the reason for calling this method of obtaining inferences a parametric method. They are based on knowing the distribution of the population random variable from which the sampling distribution of the point estimator is determined. In addition, it is generally assumed that the population, from which the sample was drawn, is normally or approximately normally distributed. This last assumption, normality, is as useful as dangerous, because of the fact that wherever this assumption is violated, it invalidates all the results of any test already performed, when sample sizes are not large enough