Explain why having low statistical power could be considered a limitation for a statistical test.

Statistical power is the likelihood that a study will detect an effect when there is an effect there to be detected. If this value is low, then the probability of making a Type II error, or concluding there is no effect is high. Hope this answers the question. Have a nice day.

Statistical Power:

The power of any statistical test is defined as the probability that it will reject a false null hypothesis. It is inversely related to beta or the probability of making a Type II error. In short, power = 1 – β. To put it another way, studies should have no more than a 20% probability of making a Type II error (recall that power = 1 – β).

How is it limited?

Statistical power is affected chiefly by the size of the effect and the size of the sample used to detect it. Bigger effects are easier to detect than smaller effects, while large samples offer greater test sensitivity than small samples. A factor called sensitivity affects the power in power analysis.  The term sensitivity refers to the number of true positives out of the total of true positives and false negatives.

Is this true of false? If so the answer is TRUE