Statistical test power

• z theoretical: compare a sample average with a theoretical value provided that you already know the population's standard deviation
• t theoretical: compare a sample average with a theoretical value without knowing the population's standard deviation. This will be calculated based on the test sample
• t Test: compare the average of two samples
• Paired t test: compares the average of two samples by comparing data in pairs (2 by 2)
• Chi2: compares the standard deviation of a sample based on a theoretical value
• F Test : compares the standard deviations of two samples
• Test 1P: compares the proportion obtained from a sample (Ex: 3 defects out of 1000 parts) based on a theoretical proportion
• Test 2P: compares 2 proportions measured from two different samples (Ex: Batch A: 2 defects out of 24, Lot B: 4 defects out of 26)
  

• The sample size that you want to use
• The expected standard deviation for your values. This standard deviation can be an estimate, for example:
• We know that the temperature varies ± 4° for normal production (expert estimate). We believe that this estimate covers 95% of the actual dispersion (± 4 sigma) so we estimate the residual standard deviation as 1.
• The alpha risk from which you will conclude that the difference is significant: generally it is advised to leave it at 0.05.
• The typical difference that you want to evaluate: in the preceding example we want to observe an average difference of 1 between the 2 samples.