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Unformatted text preview: Statistics Tutorial: Power of a Hypothesis Test The probability of not committing a Type II error is called the power of a hypothesis test. Effect Size To compute the power of the test, one offers an alternative view about the "true" value of the population parameter, assuming that the null hypothesis is false. The effect size is the difference between the true value and the value specified in the null hypothesis. Effect size = True value  Hypothesized value For example, suppose the null hypothesis states that a population mean is equal to 100. A researcher might ask: What is the probability of rejecting the null hypothesis if the true population mean is equal to 90? In this example, the effect size would be 90  100, which equals 10. Factors That Affect Power The power of a hypothesis test is affected by three factors. Sample size ( n ). Other things being equal, the greater the sample size, the greater the power of the test....
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 Summer '10
 DennisGilliland
 Statistics, Probability, Null hypothesis, Statistical hypothesis testing, researcher, Statistical significance, Type I and type II errors, Statistical power

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