Answer: D - The probability of a Type II error increases as you set the probability of a Type I error lower.
Key Takeaway: A Type II error in hypothesis testing is the probability of accepting a null hypothesis when it should be rejected. In this example, the Sprint executive accepts the null hypothesis, which means that he believes the population is younger than it actually is. This has serious implications for business problems or needs as one cannot target a population for which they are missing basic demographic characteristics. The lower the chance of rejecting the null hypothesis in error, the higher the probability of committing a Type II error.
