IT we reject the huil hypothesis, we c O A. there is enough statistical evidence to infer that the alternative hypothesis is true O B. there is not enough statistical evidence to infer that the altemative hypothesis is true OC. the test is statistically insignificant at whatever level of significance is specified O D. there is enough statistical evidence to infer that the null hypothesis is true In a given hypothesis test, the null hypothesis can be rejected at the 0.10 and the 0.05 level of significance, but cannot be rejected at the 0.01 level. The most accurate statement about the pvalue for this test O A. 0.01 < p-value < 0.05 O B. 0.05 < p-value < 0.10 O C. p-value =0.01 D. p-value = 0.10

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**Understanding Hypothesis Testing: Key Concepts**

**1. Interpreting the Null Hypothesis (H0)**
When analyzing hypothesis tests, interpreting the results often involves understanding whether to reject or fail to reject the null hypothesis. Here are key conclusions when the null hypothesis is rejected:

- **A. Alternative Hypothesis Validity:**
  - If the null hypothesis is rejected, there is sufficient statistical evidence to infer that the alternative hypothesis is true.

- **B. Insufficient Evidence for Alternative:**
  - This conclusion states there is not enough statistical evidence to infer the alternative hypothesis is true, but is incorrect if the null hypothesis is rejected.

- **C. Statistical Significance:**
  - Incorrect in this context, as rejecting H0 indicates statistical significance.

- **D. Evidence for Null Hypothesis:**
  - Incorrect conclusion if H0 is rejected, indicating a lack of support for the null hypothesis being true.

**2. p-value Interpretation in Hypothesis Testing**
The p-value assists in determining the strength of evidence against the null hypothesis. In a given hypothesis test scenario:

- The null hypothesis can be rejected at the 0.10 and 0.05 levels of significance but not at the 0.01 level. Therefore, the most accurate statement about the p-value is:
  - **A. 0.01 < p-value < 0.05:**
    - Indicates the rejection of H0 at 0.05 but not at the more stringent 0.01 level.

- Other Options:
  - **B. 0.05 < p-value < 0.10**  
  - **C. p-value = 0.01**  
  - **D. p-value = 0.10**

These concepts are crucial in understanding how statistical evidence influences the acceptance or rejection of hypotheses in data analysis and research.
Transcribed Image Text:**Understanding Hypothesis Testing: Key Concepts** **1. Interpreting the Null Hypothesis (H0)** When analyzing hypothesis tests, interpreting the results often involves understanding whether to reject or fail to reject the null hypothesis. Here are key conclusions when the null hypothesis is rejected: - **A. Alternative Hypothesis Validity:** - If the null hypothesis is rejected, there is sufficient statistical evidence to infer that the alternative hypothesis is true. - **B. Insufficient Evidence for Alternative:** - This conclusion states there is not enough statistical evidence to infer the alternative hypothesis is true, but is incorrect if the null hypothesis is rejected. - **C. Statistical Significance:** - Incorrect in this context, as rejecting H0 indicates statistical significance. - **D. Evidence for Null Hypothesis:** - Incorrect conclusion if H0 is rejected, indicating a lack of support for the null hypothesis being true. **2. p-value Interpretation in Hypothesis Testing** The p-value assists in determining the strength of evidence against the null hypothesis. In a given hypothesis test scenario: - The null hypothesis can be rejected at the 0.10 and 0.05 levels of significance but not at the 0.01 level. Therefore, the most accurate statement about the p-value is: - **A. 0.01 < p-value < 0.05:** - Indicates the rejection of H0 at 0.05 but not at the more stringent 0.01 level. - Other Options: - **B. 0.05 < p-value < 0.10** - **C. p-value = 0.01** - **D. p-value = 0.10** These concepts are crucial in understanding how statistical evidence influences the acceptance or rejection of hypotheses in data analysis and research.
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