We didn't discuss the role of sample size in this chapter as thoroughly as we did for confidence intervals in the previous chapter, but more advanced books do include sample size formulas for hypothesis testing.
Consider the situation where you are testing the null hypothesis that a population mean is less than or equal to 100 versus a one-tailed alternative.
A sample size formula might indicate the sample size needed to make the power at least 0.90 when the true mean is 103.
What are the trade-offs here? Essentially, what is the advantage of a larger sample size?