<![CDATA[Hypothesis testing is a very important part of inferential statistics. The results of this hypothesis are later used to decide or establish something in planning or in other interests. In everyday life there are often problems that require a decision to find a way out. The decision taken must be right so that the problems experienced can be resolved properly. Because if not, the decision taken will be detrimental. Decision in the sense of a hypothesis that is positive for the decision maker itself needs to be tested whether the hypothesis taken can be beneficial or even detrimental, so that a hypothesis test is needed. Based on the results of research conducted on sampling distribution, central limit theorem and parameter estimation using simulated data, several things can be concluded as follows: Hypothesis Testing for the Average of One Population, Hypothesis Testing for the Average Difference of Two Populations, Hypothesis Testing for the Proportion of One Population. In hypothesis testing for the proportion of one population, for n = 10, the proportion of sample values less than 10 is 0.4, for n = 20, the proportion of sample values less than 10 is 0.5, and for n = 30, the proportion of sample values less than 10 is 0.466667. Based on this information, it can be concluded that the proportion of the sample closest to the proportion of the actual population is at the time n = 30, because for n = 30, the proportion of sample values less than 10 is 0.466667, where the value is equal to the value of the proportion of the population. Hypothesis Testing For the Difference in Proportion of Two Populations. In hypothesis testing the difference in the proportion of the two populations, for n 1 = 20 and n 2 = 35, the difference in the proportion of sample values less than 10 is 0.507143, for n 1 = 15 and n 2 = 25, the proportion of sample values less than 10 is 0.426667, and for n 1 = 30 and n 2 = 40, the proportion of sample values less than 10 is 0.711 ]]>
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