9. Type I and Type II errors Every time you conduct a hypothesis test, there are four possible outcomes of your decision to reject or not reject the null hypothesis: (1) You don't reject the null hypothesis when it is true, (2) you reject the null hypothesis when it is true, (3) you don't reject the null hypothesis when it is false, and (4) you reject the null hypothesis when it is false. Consider the following analogy: You are a hiring manager for a large company. For every job applicant, you must decide whether to hire the applicant based on your assessment of whether he or she will be an asset to the company. Suppose your null hypothesis is that the applicant will not be an asset to the company. As in hypothesis testing, there are four possible outcomes of your decision: (1) You do not hire the applicant when the applicant will not be an asset to the company, (2) you hire the applicant when the applicant will not be an asset to the company, (3) you do not hire the applicant when the applicant will be an asset to the company, and (4) you hire the applicant when the applicant will be an asset to the company. Which of the following outcomes corresponds to a Type I error? O You do not hire the applicant when the applicant will not be an asset to the company. O You hire the applicant when the applicant will be an asset to the company. O You do not hire the applicant when the applicant will be an asset to the company. O You hire the applicant when the applicant will not be an asset to the company. Which of the following outcomes corresponds to a Type II error? O You hire the applicant when the applicant will be an asset to the company. O You hire the applicant when the applicant will not be an asset to the company. O You do not hire the applicant when the applicant will be an asset to the company. O You do not hire the applicant when the applicant will not be an asset to the company. As a hiring manager, the worst error you can make is to hire the applicant when the applicant will not be an asset to the company. The probability that you make this error, in our hypothesis testing analogy, is described by