For conducting a two-tailed hypothesis test with a certain data set, using the smaller of n,-1 and ng-1 for the degrees of freedom results in df = 11, and the coresponding critical values are t 12201. Using the formula for the exact degrees of freedom results in df= 19.063, and the corresponding critical values are t+2.093. How is using the critical values of t=+2.201 more "conservative" than using the critical values of +2.093? Choose the correct answer below. O A. Using the critical values of t= +2.201 is less likely to lead to rejection of the null hypothesis than using the critical values of 2.093 O B. Using the critical values of t= +2.201 is more likely to lead to rejection of the null hypothesis than using the critical values of 2.093. OC. Using the critical values of t= +2.201 results in rounding the test statistic to more decimal places than using the critical values of 2.093 OD. Using the critical values of t= 12.201 requires fewer calculations than using the critical values of t2.093.
For conducting a two-tailed hypothesis test with a certain data set, using the smaller of n,-1 and ng-1 for the degrees of freedom results in df = 11, and the coresponding critical values are t 12201. Using the formula for the exact degrees of freedom results in df= 19.063, and the corresponding critical values are t+2.093. How is using the critical values of t=+2.201 more "conservative" than using the critical values of +2.093? Choose the correct answer below. O A. Using the critical values of t= +2.201 is less likely to lead to rejection of the null hypothesis than using the critical values of 2.093 O B. Using the critical values of t= +2.201 is more likely to lead to rejection of the null hypothesis than using the critical values of 2.093. OC. Using the critical values of t= +2.201 results in rounding the test statistic to more decimal places than using the critical values of 2.093 OD. Using the critical values of t= 12.201 requires fewer calculations than using the critical values of t2.093.
MATLAB: An Introduction with Applications
6th Edition
ISBN:9781119256830
Author:Amos Gilat
Publisher:Amos Gilat
Chapter1: Starting With Matlab
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Transcribed Image Text:**Hypothesis Testing with Different Critical Values: An Educational Insight**
**Introduction:**
In statistical hypothesis testing, selecting the appropriate critical values is crucial for accurate decision-making. This example illustrates the impact of choosing different critical values in a two-tailed hypothesis test.
**Scenario Explanation:**
For a two-tailed hypothesis test with a certain data set, we have two different approaches for determining degrees of freedom (df):
1. **Using a conservative approach:**
- Degrees of freedom calculated as the smaller of \( n_1 - 1 \) and \( n_2 - 1 \), resulting in \( df = 11 \).
- Corresponding critical values: \( t = \pm 2.201 \).
2. **Using the formula for exact degrees of freedom:**
- Results in \( df = 19.063 \).
- Corresponding critical values: \( t = \pm 2.093 \).
**Task:**
Determine why using the critical values of \( t = \pm 2.201 \) is more "conservative" compared to the critical values of \( \pm 2.093 \).
**Options:**
Choose the correct answer from the following:
**A.** Using the critical values of \( t = \pm 2.201 \) is less likely to lead to rejection of the null hypothesis than using the critical values of \( \pm 2.093 \).
**B.** Using the critical values of \( t = \pm 2.201 \) is more likely to lead to rejection of the null hypothesis than using the critical values of \( \pm 2.093 \).
**C.** Using the critical values of \( t = \pm 2.201 \) results in rounding the test statistic to more decimal places than using the critical values of \( \pm 2.093 \).
**D.** Using the critical values of \( \pm 2.201 \) requires fewer calculations than using the critical values of \( \pm 2.093 \).
**Analysis:**
Choosing a more conservative critical value (higher in absolute terms) like \( \pm 2.201 \) generally results in a lower likelihood of rejecting the null hypothesis, favoring the maintenance of the null until there is strong enough evidence against it. This approach reduces the risk of a Type I error, aligning with a more cautious stance in hypothesis testing.
**Conclusion:**
Understanding
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