Testing of Statistical Hypotheses

Milan Holický
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Milan Holický: Czech Technical University in Prague, Klokner Institute, Department of Structural Reliability

Chapter Chapter 10 in Introduction to Probability and Statistics for Engineers, 2013, pp 125-138 from Springer

Abstract: Abstract The testing of statistical hypotheses is one of the essential topics of mathematical statistics, and is often used in engineering and scientific applications. In general, a given hypothesis about a population based on limited sample data is verified specifying a certain high probability (0.95) that the hypothesis is accepted. The complementary small probability (0.05), called significance level, is the probability that the hypothesis will be rejected, even though it is correct (Type I error). Another error may occur when the hypothesis is accepted, although incorrect (Type II error). Operational techniques are provided for testing the deviation of a sample mean from the population mean, testing the deviation of a sample variance from the population variance, testing the difference between two sample means, and testing difference between two sample variances. Two additional frequently applied tests are included: tests of good fit of a given theoretical model, and the testing of outliers in a sample.

Keywords: Limited Sample Data; Dixon Test; Grubbs Test; Rejection Area; Strict Significance Level (search for similar items in EconPapers)
Date: 2013
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-642-38300-7_10

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DOI: 10.1007/978-3-642-38300-7_10

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