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Inferential Statistics in Business

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In today’s world, we are faced with situations everyday where Statistics can be applied. In general, Statistics is the science of collecting, organizing, and analyzing numerical data. The techniques involved in Statistics are important for the work of many professions, thus the proper preparation and theoretical background of Statistics is valuable for many successful career paths. Marketing campaigns, the realm of gambling, professional sports, the world of business and economics, the political domain, education, and forecasting future occurrences are all areas which fundamentally rely on the use of Statistics. Statistics is a broad subject that branches off into several categories. In particular, Inferential Statistics contains two …show more content…

The distribution of the test statistic under the null-hypothesis is derived from the assumptions identified previously. Common test statistics may follow the following distributions: Normal, Student T, and Chi-Square. This distribution separates the possible values of the estimator into two categories: values for which the null-hypothesis is accepted or rejected. The region for which we accept the null-hypothesis is called the critical region and the area underneath the curve that corresponds to the critical region is known as the level of confidence. Hence, we can develop a confidence interval for which we can see the lowest and highest point of the critical region. Any observed sample mean that lies outside of this confidence interval (outside the critical region) would cause us to reject the null-hypothesis in favor of the alternative hypothesis. The area of the rejection region is known as the level of significance and represents type I error (alpha) corresponding to the probability that a true null-hypothesis is rejected (as opposed to type II error- beta; the probability of accepting a false null-hypothesis). Essentially, hypothesis testing calls for comparing a test statistic to the critical value of the test statistic. If this test statistic is greater than the critical value of the test statistic, we will reject the null hypothesis in favor of the alternative hypothesis. If

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