We have this situation: Ho: H = 100 H: H > 100 a = 0.05 n = 32 What critical value forms the rejection region?

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**Hypothesis Testing: Finding the Critical Value**

In this example, we will explore a situation involving hypothesis testing. We are given the following conditions:

- Null Hypothesis (\(H_0\)): \(\mu = 100\)
- Alternative Hypothesis (\(H_a\)): \(\mu > 100\)
- Significance Level (\(\alpha\)): 0.05
- Sample Size (\(n\)): 32

The challenge is to determine the critical value that forms the rejection region for this hypothesis test.

**Choices Provided:**
1. \( \boxed{z = 1.645} \)
2. \( \boxed{z = 1.960} \)
3. \( \boxed{t = 1.960} \)
4. \( \boxed{t = 1.645} \)

In hypothesis testing for a population mean, the critical value determines the threshold at which we reject the null hypothesis in favor of the alternative hypothesis. The choice of \(z\)-score or \(t\)-score depends on the sample size and whether the population standard deviation is known.

**Explanation of Choices:**
- \(z\)-scores are typically used when the sample size is large (usually \(n \geq 30\)) and/or the population standard deviation is known.
- \(t\)-scores are used when the sample size is small and/or the population standard deviation is unknown.

Given:
- The sample size here is 32, which is typically considered large enough to use the \(z\)-distribution.

Since we are performing a one-tailed test at \(\alpha = 0.05\), the critical \(z\)-value that corresponds to this significance level (5%) in a standard normal distribution is approximately 1.645.

Therefore, the appropriate critical value for this scenario is:
\[ \boxed{z = 1.645} \]

In conclusion, the value \(z = 1.645\) forms the rejection region for this hypothesis test, meaning that if the test statistic exceeds 1.645, we reject the null hypothesis in favor of the alternative hypothesis.
Transcribed Image Text:**Hypothesis Testing: Finding the Critical Value** In this example, we will explore a situation involving hypothesis testing. We are given the following conditions: - Null Hypothesis (\(H_0\)): \(\mu = 100\) - Alternative Hypothesis (\(H_a\)): \(\mu > 100\) - Significance Level (\(\alpha\)): 0.05 - Sample Size (\(n\)): 32 The challenge is to determine the critical value that forms the rejection region for this hypothesis test. **Choices Provided:** 1. \( \boxed{z = 1.645} \) 2. \( \boxed{z = 1.960} \) 3. \( \boxed{t = 1.960} \) 4. \( \boxed{t = 1.645} \) In hypothesis testing for a population mean, the critical value determines the threshold at which we reject the null hypothesis in favor of the alternative hypothesis. The choice of \(z\)-score or \(t\)-score depends on the sample size and whether the population standard deviation is known. **Explanation of Choices:** - \(z\)-scores are typically used when the sample size is large (usually \(n \geq 30\)) and/or the population standard deviation is known. - \(t\)-scores are used when the sample size is small and/or the population standard deviation is unknown. Given: - The sample size here is 32, which is typically considered large enough to use the \(z\)-distribution. Since we are performing a one-tailed test at \(\alpha = 0.05\), the critical \(z\)-value that corresponds to this significance level (5%) in a standard normal distribution is approximately 1.645. Therefore, the appropriate critical value for this scenario is: \[ \boxed{z = 1.645} \] In conclusion, the value \(z = 1.645\) forms the rejection region for this hypothesis test, meaning that if the test statistic exceeds 1.645, we reject the null hypothesis in favor of the alternative hypothesis.
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