Describe a Type I & Type II Error for each scenario.
$\left(B\right)$ A computer repair store advertises the average repair cost as \$75 or less.

• A. Type I: We conclude the average repair cost is more than \$75 when it's actually \$75 or less.Type II: We conclude the average repair cost is \$75 or less when it's more than that.
• B. Type I: We conclude the average repair cost is more than \$75 when it's actually \$75 or less.Type II: We conclude the average repair cost is more than \$75 when it's actually \$75 or less.
• C. Type I: We conclude the average repair cost is \$75 when it's actually more than \$75.Type II: We conclude the average repair cost is \$75 or less when it's actually \$75 or less.
• D. Type I: We conclude the average repair cost is \$75 or less when it's more than that.Type II: We conclude the average repair cost is \$75 or less when it's more than that.

Answer: (A)

Verified step by step guidance

1. Step 1: Identify the null hypothesis (\(H_0\)) and the alternative hypothesis (\(H_a\)) based on the scenario. Here, the store advertises the average repair cost as \$75 or less, so we set \(H_0: \mu \leq 75\) and \(H_a: \mu > 75\).
2. Step 2: Understand what a Type I error means in hypothesis testing. A Type I error occurs when we reject the null hypothesis \(H_0\) even though it is true. In this context, it means concluding that the average repair cost is more than \$75 when it is actually \$75 or less.
3. Step 3: Understand what a Type II error means. A Type II error happens when we fail to reject the null hypothesis \(H_0\) even though the alternative hypothesis \(H_a\) is true. Here, it means concluding that the average repair cost is \$75 or less when it is actually more than \$75.
4. Step 4: Express the Type I error clearly: "Rejecting \(H_0\) (concluding average cost > \$75\() when in reality \)\mu \leq 75$."
5. Step 5: Express the Type II error clearly: "Failing to reject \(H_0\) (concluding average cost \(\leq 75\)) when in reality \(\mu > 75\)."