The figure below shows a randomization distribution based on 1000 simulated samples for hypotheses Ho:μ₁ = µ₂ VS H₁: M₁ M₂. The statistic used for each sample is D = ₁-₂. Which sample provides the strongest evidence against Ho? OD= 2.3 Aanbe 90- 80- 70 60 50 40 30- 20 10- 0 OD = 1.4 O Both provide equally strong evidence against Ho 0 Differences 1 2

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### Randomization Distribution for Hypothesis Testing

The figure below shows a randomization distribution based on 1000 simulated samples for hypotheses \( H_0: \mu_1 = \mu_2 \) vs \( H_a: \mu_1 \neq \mu_2 \). The statistic used for each sample is \( D = \bar{X}_1 - \bar{X}_2 \). Which sample provides the strongest evidence against \( H_0 \)?

![Graph showing randomization distribution](image-link)

#### Graph Description:
- **X-axis (Differences):** Represents the differences (\( D \)) between the sample means, ranging from -3 to 3.
- **Y-axis (Frequency):** Represents the frequency of each difference observed in the 1000 simulated samples.
- **Distribution Shape:** The distribution is approximately normal, centered around 0, indicating that under the null hypothesis, the differences between the sample means are likely to be close to zero.

#### Question:
Which sample provides the strongest evidence against \( H_0 \)?

- \( D = 2.3 \)
- \( D = 1.4 \)
- Both provide equally strong evidence against \( H_0 \)

**Considerations:** 
- Larger absolute values of \( D \) indicate stronger evidence against \( H_0 \).
- Compare the given \( D \) values to the distribution to determine which is further from the center (0).

### Answer:
* \( D = 2.3 \) provides the strongest evidence against \( H_0 \).

This inference is based on the fact that \( D = 2.3 \) falls further from the center of the distribution compared to \( D = 1.4 \), suggesting it is less likely to occur under the null hypothesis.
Transcribed Image Text:### Randomization Distribution for Hypothesis Testing The figure below shows a randomization distribution based on 1000 simulated samples for hypotheses \( H_0: \mu_1 = \mu_2 \) vs \( H_a: \mu_1 \neq \mu_2 \). The statistic used for each sample is \( D = \bar{X}_1 - \bar{X}_2 \). Which sample provides the strongest evidence against \( H_0 \)? ![Graph showing randomization distribution](image-link) #### Graph Description: - **X-axis (Differences):** Represents the differences (\( D \)) between the sample means, ranging from -3 to 3. - **Y-axis (Frequency):** Represents the frequency of each difference observed in the 1000 simulated samples. - **Distribution Shape:** The distribution is approximately normal, centered around 0, indicating that under the null hypothesis, the differences between the sample means are likely to be close to zero. #### Question: Which sample provides the strongest evidence against \( H_0 \)? - \( D = 2.3 \) - \( D = 1.4 \) - Both provide equally strong evidence against \( H_0 \) **Considerations:** - Larger absolute values of \( D \) indicate stronger evidence against \( H_0 \). - Compare the given \( D \) values to the distribution to determine which is further from the center (0). ### Answer: * \( D = 2.3 \) provides the strongest evidence against \( H_0 \). This inference is based on the fact that \( D = 2.3 \) falls further from the center of the distribution compared to \( D = 1.4 \), suggesting it is less likely to occur under the null hypothesis.
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