|PDF Title||:||Statistical Power Analysis|
|Author||:||Kevin R. Murphy & Brett Myors|
|Publisher||:||Lawrence Erlbaum Associates|
|Total Page||:||172 Pages|
|PDF Size||:||9.78 MB|
|PDF Link||:||Read and Download|
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“When testing minimum-effect null hypotheses, seriously consider the possibility that the null hypothesis will be true. This opens the door for what is sometimes considered statistical heresy (i.e., accepting the null).
It also opens the door to the possibility that Type I errors will be made, which means that alpha levels, statistical tests, balancing Type I and Type II errors, and so on have some real meaning in this context. Power analysis has many benefits.
It helps researchers make informed decisions about the design of their own research (especially about the number of cases needed and the choice of alpha levels), and it also helps them make sense out of other researchers’ significant or nonsignificant results.
However, the indirect benefits of power analysis may be the most important. Power analysis is likely to lead them to use larger samples, which in turn will often encourage them to use better measures, more pretests, more carefully designed procedures, and so on.
Power analysis also helps to focus attention on effect sizes, rather than focusing exclusively on the p value associated with some statistical test. All of this is likely to improve the quality and consistency of social science research.”
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