# statistical power

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## statistical power

the probability of supporting a research hypothesis when it is true. Fisher's (1925) approach to inferential statistics was that a Null hypothesis should be tested. If the results were statistically significant then the Null hypothesis could be rejected. Neyman and Pearson (1928) pointed out that there is a second hypothesis involved in significance testing: the research hypothesis. If the results of a study achieve statistical significance then researchers may be committing a Type I error by rejecting the Null Hypothesis when it is in fact true. On the other hand, Neyman and Pearson noted that when a result fails to reach statistical significance a Type II error (which they called an error of the second type) may be committed: the failure to reject the Null hypothesis when the research hypothesis is true. Greater statistical power lessens the likelihood of committing a Type II error.

Many writers (e.g. Cohen, 1988) argue that failure to consider statistical power renders statistical significance testing relatively meaningless.

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I did not talk with anyone who initially thought, like I did, about statistical power.
Statistical power will be used to evaluate the impact of increasing x and the resulting experimental design.
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The simulation study for experimental designs was conducted to examine the accuracy of predicting the interaction effects and to estimate the corresponding statistical power, the probability of accepting true interaction effects, by the method.
Statistical power of at least 90% was achieved allowing detection of a 30% change in quality of life at a 0.05.

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