For the following conclusions of hypotheses tests, write down if it is a Type I or Type II error, if any error was committed. Use a = 0.05 for all parts. For a one-sample t-test testing Ho: ≥ 5.5 vs. HA μ< 5.5, the p-value was 0.0025 and u was found to be 3.4. >#2, the p-value was 0.28, b.a For a independent two-sample t-test testing Ho: 1 ≤ ₂ VS. HA #₁ was found to be 24, and 2 was found to be 18.

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**Hypothesis Testing: Identifying Errors**

For the following conclusions of hypothesis tests, determine if a Type I or Type II error was committed, if any error occurred. Use a significance level of \(\alpha = 0.05\) for all parts.

**a.** For a one-sample t-test testing \(H_0: \mu \geq 5.5\) vs. \(H_A: \mu < 5.5\), the p-value was 0.0025 and \(\mu\) was found to be 3.4.

**b.** For an independent two-sample t-test testing \(H_0: \mu_1 \leq \mu_2\) vs. \(H_A: \mu_1 > \mu_2\), the p-value was 0.28, \(\mu_1\) was found to be 24, and \(\mu_2\) was found to be 18. 

*Note: In hypothesis testing, a Type I error occurs when the null hypothesis is true but is incorrectly rejected. A Type II error occurs when the null hypothesis is false but is incorrectly not rejected.*
Transcribed Image Text:**Hypothesis Testing: Identifying Errors** For the following conclusions of hypothesis tests, determine if a Type I or Type II error was committed, if any error occurred. Use a significance level of \(\alpha = 0.05\) for all parts. **a.** For a one-sample t-test testing \(H_0: \mu \geq 5.5\) vs. \(H_A: \mu < 5.5\), the p-value was 0.0025 and \(\mu\) was found to be 3.4. **b.** For an independent two-sample t-test testing \(H_0: \mu_1 \leq \mu_2\) vs. \(H_A: \mu_1 > \mu_2\), the p-value was 0.28, \(\mu_1\) was found to be 24, and \(\mu_2\) was found to be 18. *Note: In hypothesis testing, a Type I error occurs when the null hypothesis is true but is incorrectly rejected. A Type II error occurs when the null hypothesis is false but is incorrectly not rejected.*
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