To determine whether to reject the null hypothesis (H₀), you need to consider the following steps based on your test results:

1. Set Significance Level (Alpha): Determine the significance level (α), often set at 0.05 or 0.01, depending on the test’s strictness.

2. Calculate or Identify the p-value: This is the probability of observing a result as extreme as the test statistic under H₀.

3. Compare p-value to Alpha:

   • If p-value ≤ α: Reject H₀. This suggests that there is statistically significant evidence to support the alternative hypothesis (H₁).
   • If p-value > α: Do not reject H₀. This suggests insufficient evidence to support H₁, so you retain H₀.

Would you like further details on this process, or do you have specific data for analysis?

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Additional Questions to Consider:

1. What significance level (α) is being used in this test?
2. What is the calculated p-value, and how does it compare to α?
3. If rejecting H₀, what are the potential implications of this conclusion?
4. How does the sample size impact the reliability of the p-value?
5. Are there any potential sources of error in this hypothesis test?

Tip:

Always state your hypothesis clearly before testing and ensure that your decision rule (comparing p-value to α) is established upfront to avoid bias in interpretation.