Identify situations in which it is important to estimate power

Suppose you work for a foundation whose mission
is to support researchers in mathematics education and your role
is to evaluate grant proposals and decide which ones to fund.
You receive a proposal to evaluate a new method of teaching high-school
algebra. The research plan is to compare the achievement of
students taught by the new method with the achievement of students taught by the traditional
method. The proposal contains good theoretical arguments why the
new method should be superior and the proposed methodology is
sound. In addition to these positive elements, there is one
important question still to be answered: Does the experiment have
a high probability of providing strong evidence that the new method
is better than the standard method if, in fact, the new method
is actually better? It is possible, for example, that the proposed
sample size is so small that even a fairly large population difference
would be difficult to detect. That is, if the sample size is
small, then even a fairly large difference in sample means might
not be significant. If the difference is not significant,
then no strong conclusions can be drawn about the population means.
It is not justified to conclude that the null hypothesis that
the population means
are equal is true just because the difference is not significant.
Of course, it is not justified to conclude that this null hypothesis
is false. Therefore, when an effect is not significant, the result is inconclusive. You may prefer
that your foundation's money be used to fund a project that has
a higher probability of being able to make a strong conclusion.

Power is defined as the probability of correctly
rejecting a false null hypothesis. In terms of our example, it
is the probability that given there is a difference between
the population means of the new method and the standard method,
the sample means will be significantly different. The probability
of failing to reject a false null hypothesis is often referred
to as β.
Therefore power can be defined as:

power = 1 - β.

It is very important to consider power while designing
an experiment. You should avoid spending a lot of time and/or
money on an experiment that has little chance of finding a significant effect.