Power Analysis is an important aspect of experimental design. It allows us to determine the sample size required to detect an effect of a given size with a given degree of confidence.
There are four parameters involved in a power analysis. The research must ‘know’ 3 and solve
for the 4th.
1. Alpha:
Probability of finding significance where there is none
False positive
Probability of a Type I error
Usually set to.05
2. Power
Probability of finding true significance
True positive
1 – beta, where beta is:
Probability of not finding significance when it is there
False negative
Probability of a Type II error
Usually set to.80
3. N:
The sample size (usually the parameter you are solving for)
May be known and fixed due to study constraints
4. Effect size:
Usually, the ‘expected effect’ is ascertained from:
Pilot study results
Published findings from a similar study or studies
May need to be calculated from results if not reported
May need to be translated as design specific using rules of thumb
Field defined ‘meaningful effect’
Educated guess (based on informal observations and knowledge of the
field)
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If you want to look for more information, check some free online courses available at coursera.org, edx.org or udemy.com.
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