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.

Recommended reading list:

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