A null hypothesis is not rejected at a given level of significance. As the assumed value of the mean gets further away from the true population mean, the Type Il error will Multiple Choice decrease stay the same

As the assumed value of the mean diverges further from the true population mean, the likelihood of a Type II error, or failing to reject a false null hypothesis, actually increases. This is because the test becomes less sensitive to detecting a difference when there's a larger discrepancy, leading to more instances where we mistakenly accept a false null hypothesis. Conversely, if you reposition your assumed mean closer to the true population mean, you improve your chances of correctly rejecting the null hypothesis when it should be rejected. This illustrates the delicate dance between assumptions and statistical power in hypothesis testing!