Does 10K running time decrease when the runner listens to music? Nine runners were timed as they ran a 10K with and without listening to music. The running times in minutes are shown below. Running Time 45 36 42 37 52 47 40 53 50 With Music Without 46 37 47 38 54 44 43 51 53 Music Assume a Normal distribution. What can be concluded at the the a = 0.10 level of significance?

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**Title: Investigating the Impact of Listening to Music on 10K Running Times**

**Overview:**

This study seeks to determine if listening to music affects the 10K running times of nine runners. Each runner's time was recorded with and without music. The following data reflects their running times in minutes:

**Running Time Data:**

- **With Music:** 45, 36, 42, 37, 52, 47, 40, 53, 50
- **Without Music:** 46, 37, 47, 38, 54, 44, 43, 51, 53

**Statistical Analysis:**

To test the hypothesis, a normal distribution model is assumed with a significance level (α) set at 0.10.

**Steps to Conduct the Test:**

a. **Hypotheses Setup:**
   - **Null Hypothesis (H₀):** There is no difference in mean running times with and without music.
   - **Alternative Hypothesis (H₁):** There is a significant difference in mean running times, possibly indicating shorter times with music.

b. **Calculating the Test Statistic:**
   - Compute the test statistic using the mean and standard deviation of the running times, ensuring the result is accurate to three decimal places.

c. **Determining the P-value:**
   - Calculate the p-value using the test statistic, rounding to four decimal places.

d. **Comparison to α:**
   - Determine if the p-value is less than or equal to α to decide whether to reject the null hypothesis.

e. **Conclusion Based on Data:**
   - **Reject the Null Hypothesis:** If the p-value ≤ α, suggesting music might influence running time.
   - **Fail to Reject the Null Hypothesis:** If the p-value > α, indicating insufficient evidence to conclude music affects running time.

**Interpretation:**

g. **P-value Contextualization:**
   - Explained in terms of the probability under the null hypothesis that the observed effect is purely by chance.

h. **Significance Level Contextualization:**
   - Relates the chance of incorrectly rejecting the null hypothesis (Type I error) to the significance level (α = 0.10).

This educational content helps students understand hypothesis testing, statistical analysis, and interpreting p-values in the context of real-world research scenarios.
Transcribed Image Text:**Title: Investigating the Impact of Listening to Music on 10K Running Times** **Overview:** This study seeks to determine if listening to music affects the 10K running times of nine runners. Each runner's time was recorded with and without music. The following data reflects their running times in minutes: **Running Time Data:** - **With Music:** 45, 36, 42, 37, 52, 47, 40, 53, 50 - **Without Music:** 46, 37, 47, 38, 54, 44, 43, 51, 53 **Statistical Analysis:** To test the hypothesis, a normal distribution model is assumed with a significance level (α) set at 0.10. **Steps to Conduct the Test:** a. **Hypotheses Setup:** - **Null Hypothesis (H₀):** There is no difference in mean running times with and without music. - **Alternative Hypothesis (H₁):** There is a significant difference in mean running times, possibly indicating shorter times with music. b. **Calculating the Test Statistic:** - Compute the test statistic using the mean and standard deviation of the running times, ensuring the result is accurate to three decimal places. c. **Determining the P-value:** - Calculate the p-value using the test statistic, rounding to four decimal places. d. **Comparison to α:** - Determine if the p-value is less than or equal to α to decide whether to reject the null hypothesis. e. **Conclusion Based on Data:** - **Reject the Null Hypothesis:** If the p-value ≤ α, suggesting music might influence running time. - **Fail to Reject the Null Hypothesis:** If the p-value > α, indicating insufficient evidence to conclude music affects running time. **Interpretation:** g. **P-value Contextualization:** - Explained in terms of the probability under the null hypothesis that the observed effect is purely by chance. h. **Significance Level Contextualization:** - Relates the chance of incorrectly rejecting the null hypothesis (Type I error) to the significance level (α = 0.10). This educational content helps students understand hypothesis testing, statistical analysis, and interpreting p-values in the context of real-world research scenarios.
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