Question

Explain in your own words the “power of a statistical test”. Do you proceed with the test if you don’t have enough power”? How does the “power” affect the results of the test?

Answer 1

The power of any test of statistical significance is defined as the probability that it will reject a false null hypothesis. Statistical power is inversely related to beta or the probability of making a Type II error. In short, power = 1 – β

in own words: statistical power is the likelihood that a study will detect an effect when there is an effect there to be detected. If statistical power is high, the probability of making a Type II error, or concluding there is no effect when, in fact, there is one, goes down.

No, you don't proceed with the test if you don’t have enough power. Since it will increase the chance of committing type II error.

The desired power level affects the power in analysis to a great extent. The desired power level is typically 0.80, but the researcher performing power analysis can specify the higher level, such as 0.90, which means that there is a 90% probability the researcher will not commit a type II error.