Sample size Calculations (Power Calculations)

Testing with a to small sample size introduces a risk to falsely detect a difference or not detecting a difference. To reduce this risk it is important to do a sample size calculation. The bigger the samples size is the better the detection is. When using a samples size calculation it is known what the risk is and minimizes the amount of samples for a good power.

The smaller the difference to detect the bigger the sample size must be.

A good start for the sample size is between 20 and 40. With this amount a distribution test can be carried out. Perform the test and check the sample size.

If the test is significant. And if the amount of samples is smaller than the calculated minimum sample size, increase the amount of samples to the minimum samples size.

If the test is not significant. When a test is not significant increasing the sample size will not help to make the difference significant. Increasing the sample size will reduce the risk on false detection, but increase the sample size with care because it is possible that is waste of testing.

Develve is capable to calculate the minimum sample size of the following statistical tests:

• One sample t-test
• 2 sample t-test
• Variation F-test
• 1 sample Wilcoxon median test
• 2 sample Mann-Whitney median test
• One way Anova
• 1 sample Proportion calculation
• 2 sample Proportion calculation

Links

• http://en.wikipedia.org/wiki/Statistical_power
• http://www.itl.nist.gov/div898/handbook/ppc/section1/ppc134.htm
• http://www.itl.nist.gov/div898/handbook/prc/section2/prc222.htm
• http://www.itl.nist.gov/div898/handbook/ppc/section3/ppc333.htm