The decision of a hypothesis test based on the p-value generally depends on whether the p-value is less than or greater than the significance level, often denoted as alpha (α). Here’s the breakdown:

• If p-value ≤ α: We reject the null hypothesis (H0).
• If p-value > α: We do not reject the null hypothesis (H0).

Answer

Without the exact p-value and significance level, here are the general conclusions you could make:

• (b) Reject H0 if p-value ≤ α.
• (d) Do not reject H0 if p-value > α.

If you have specific values for the p-value and α, I can provide a more definitive answer.

Would you like further details or have any questions on hypothesis testing?

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Here are 5 related questions to consider:

1. What is the significance level (α) typically set at in hypothesis testing?
2. How is the p-value calculated for different types of statistical tests?
3. What is the impact of Type I and Type II errors in hypothesis testing?
4. How does sample size affect the p-value and test outcome?
5. Can you reject H0 if the p-value is close to, but not exactly below, α?

Tip: Always compare the p-value to the significance level to make a clear decision about H0.