Abstract: This chapter introduces the statistical hypothesis testing, which is concerned with using data to test the plausibility of a specified hypothesis. Such test might reject the hypothesis that fewer than 44 percent of Midwestern lakes are afflicted by acid rain. The chapter introduces the concept of the p-value, which measures the degree of plausibility of the hypothesis after the data have been observed. A variety of hypothesis tests concerning the parameters of both one and two normal populations are considered. Hypothesis tests concerning Bernoulli and Poisson parameters are also presented. A statistical hypothesis is usually a statement about a set of parameters of a population distribution. It is called a hypothesis because it is not known whether or not it is true. A primary problem is to develop a procedure for determining whether or not the values of a random sample from this population are consistent with the hypothesis.
Publication Year: 2009
Publication Date: 2009-01-01
Language: en
Type: book-chapter
Indexed In: ['crossref']
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Cited By Count: 1
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