Answer:
The answer is: Increases with more tests (trials)
Explanation:
The statement above is related to the subject of "Statistics." Under this is the<u> "null hypothesis."</u> This refers to the general statement which states<u> that there is no significant difference among variables. </u>The "power of the test" refers to the probability that you have rejected the null hypothesis correctly.
<em>You only find the power of the test when you are assuming that the null hypothesis is, indeed, false.</em> There are many factors affecting the power of a test such as the <em>significance level (α = alpha) of the test, the sample size (n), the difference between a parameter's true value and hypothesized value and the inherent variability of the response variables that are being measured. </em>
So, this means that the power of the study increases with more tests (trials). The process is being improved with more tests, thereby increasing the power. This, in turn, increases the probability of rejecting the null hypothesis.
Thus, this explains the answer.
