The following data represent the results from an independent-measures study comparing two treatment conditions. Treatment Response One 6.8 One 5.4 One 8.5 One 6.8 One 7.5 5.3 7.1 7.3 3.6 2.3 One One One Two Two Two Two Two Two Two Two Treatment Treatment One Two 3.6 2.3 3 4.2 6.8 5.4 8.5 6.8 7.5 5.3 7.1 7.3 3 4.2 1.9 1.3 3.2 4.9 F-ratio: p-value: 0.000 1.9 1.3 3.2 4.9 Conduct ANOVA with a = 0.05 for this data and calculate the F-ratio and p-value. Round your answer to three decimal places. Assume all population and ANOVA requirements are met. Or Now, conduct a t-test with a = 0.05 on the same data and calculate the t-statisitc and p-value. Round your answer to three decimal places. t-statistic: p-value: 0.000 ✓0¹ Observing the p-values, was there a difference in the result of the hypothesis test? No, the p-values aren't that far off where it would change the outcome of the test. O Yes, the p-values are different enough to change the outcome of the test.

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The following data represent the results from an independent-measures study comparing two treatment conditions.

| Treatment | Response |
|---|---|
| One | 6.8 |
| One | 5.4 |
| One | 8.5 |
| One | 6.8 |
| One | 7.5 |
| One | 5.3 |
| One | 7.1 |
| One | 7.3 |
| Two | 3.6 |
| Two | 2.3 |
| Two | 3 |
| Two | 4.2 |
| Two | 1.9 |
| Two | 1.3 |
| Two | 3.2 |
| Two | 4.9 |

A table summarizes the data for Treatment One and Treatment Two:

| Treatment One | Treatment Two |
|---|---|
| 6.8 | 3.6 |
| 5.4 | 2.3 |
| 8.5 | 3 |
| 6.8 | 4.2 |
| 7.5 | 1.9 |
| 5.3 | 1.3 |
| 7.1 | 3.2 |
| 7.3 | 4.9 |

**Conduct ANOVA** with \(\alpha = 0.05\) for this data and calculate the \(F\)-ratio and \(p\)-value. Round your answer to three decimal places. Assume all population and ANOVA requirements are met.

- \(F\)-ratio: [Blank box]
- \(p\)-value: 0.000 (indicated correct)

**Now, conduct a t-test** with \(\alpha = 0.05\) on the same data and calculate the \(t\)-statistic and \(p\)-value. Round your answer to three decimal places.

- \(t\)-statistic: [Blank box]
- \(p\)-value: 0.000 (indicated correct)

**Observing the p-values, was there a difference in the result of the hypothesis test?**

- [Selected] No, the p-values aren't that far off where it would change the outcome of the test.
- Yes, the p-values are different enough to change the outcome of the test.
Transcribed Image Text:The following data represent the results from an independent-measures study comparing two treatment conditions. | Treatment | Response | |---|---| | One | 6.8 | | One | 5.4 | | One | 8.5 | | One | 6.8 | | One | 7.5 | | One | 5.3 | | One | 7.1 | | One | 7.3 | | Two | 3.6 | | Two | 2.3 | | Two | 3 | | Two | 4.2 | | Two | 1.9 | | Two | 1.3 | | Two | 3.2 | | Two | 4.9 | A table summarizes the data for Treatment One and Treatment Two: | Treatment One | Treatment Two | |---|---| | 6.8 | 3.6 | | 5.4 | 2.3 | | 8.5 | 3 | | 6.8 | 4.2 | | 7.5 | 1.9 | | 5.3 | 1.3 | | 7.1 | 3.2 | | 7.3 | 4.9 | **Conduct ANOVA** with \(\alpha = 0.05\) for this data and calculate the \(F\)-ratio and \(p\)-value. Round your answer to three decimal places. Assume all population and ANOVA requirements are met. - \(F\)-ratio: [Blank box] - \(p\)-value: 0.000 (indicated correct) **Now, conduct a t-test** with \(\alpha = 0.05\) on the same data and calculate the \(t\)-statistic and \(p\)-value. Round your answer to three decimal places. - \(t\)-statistic: [Blank box] - \(p\)-value: 0.000 (indicated correct) **Observing the p-values, was there a difference in the result of the hypothesis test?** - [Selected] No, the p-values aren't that far off where it would change the outcome of the test. - Yes, the p-values are different enough to change the outcome of the test.
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