Abstract
This chapter discusses different steps in testing of statistical hypotheses. The first step in testing statistical hypotheses is to formulate a statistical model that can represent the empirical phenomenon being studied and identify the subfamily of distributions corresponding to the hypothesis under consideration. The hypothesis being tested corresponds to the subfamily of binomial distributions. Significant deviations lead to weakening of the hypotheses or to their rejection. This testing of the significance of deviations is generally done by constructing a test statistic based on the sample values, deriving the sampling distribution of the test statistic according to the model and the values of the parameters specified by the hypothesis, and rejecting the hypothesis if the observed value of the test statistic lies in an improbable region under the hypothesis. A small value of the observed significant level means either that an improbable event has occurred or that the sample data are incompatible with the hypothesis being tested.
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