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_1) based on the claim. Here, the store claims the average repair cost is \$75 or less, so set up: \(H_0: \mu \leq 75\) (the average cost is \$75 or less) \(H_1: \mu > 75\) (the average cost is more than \$75)$.
2. Step 2: Define a Type I error in this context. A Type I error occurs when we reject the null hypothesis even though it is true. So, it means concluding that the average repair cost is more than \$75 when in reality it is \$75 or less.
3. Step 3: Define a Type II error in this context. A Type II error happens when we fail to reject the null hypothesis even though the alternative hypothesis is true. So, it means concluding that the average repair cost is \$75 or less when in fact it is more than \$75.
4. Step 4: Summarize the errors clearly: - Type I error: Saying the average repair cost is more than \$75 when it is actually \$75 or less. - Type II error: Saying the average repair cost is \$75 or less when it is actually more than \$75.
5. Step 5: Understand the business implications of these errors. For example, a Type I error might unfairly damage the store's reputation by suggesting higher costs, while a Type II error might mislead customers about potential higher costs.