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Power
Analysis
Traditionally, data collected
in a research study is submitted to a significance test to assess the
viability of the null hypothesis. The p-value provided by the significance
test, and used to reject the null hypothesis, is a function of three factors:
The larger the observed effect, the larger the sample size, and/or the
more liberal the criterion required for significance (alpha ), the more
likely it is that the test will yield a significant p-value.
A power analysis, executed
when the study is being planned, is used to anticipate the likelihood
that the study will yield a significant effect and is based on the same
factors as the significance test itself. Specifically, the larger the
effect size used in the power analysis, the larger the sample size, and/or
the more liberal the criterion required for significance (alpha), the
higher the expectation that the study will yield a statistically significant
effect.
These three factors,
together with power, form a closed system - once any three are established,
the fourth is completely determined. The goal of a power analysis is to
find an appropriate balance among these factors by taking into account
the substantive goals of the study, and the resources available to the
researcher.
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