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# What Statistical Power Means

Power is a term that is used quite frequently to describe statistical tests. As is often the case, the word has a rather specific definition which we will attempt to describe here. Due to their close relation to the definition of power, we will also briefly describe the various types of errors that statistical tests can make. Thus, the probability you will reject when it is true. This type of error is called Type I Error. the probability you will accept when it false. This type of error is called Type II Error.
Power , the probability the test will reject when it is false. Thus, the more power, the higher probability of correctly rejecting .

You can increase power by increasing the sample size, , for the test. This is because the larger sample size will decrease the variance of the estimated parameters. For example, consider as an estimate of . By the central limit theorem, the variance of , where E and Var for independent and identically distributed samples from any distribution, is approximately , which gets smaller as gets larger.

An example of this is shown in Figures 3.3.1 and 3.3.2.  Subsections    Next: Numerical Approximations of Power Up: How to ask questions Previous: Comparing Two Samples: Classifying   Index

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Frank Starmer 2004-05-19
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