Explanation

In statistical hypothesis testing:

• P-value (1.4%): The probability of observing a result as extreme as, or more extreme than, the observed result if the null hypothesis were true
• Significance level (1%): The threshold set by the researcher for rejecting the null hypothesis

Decision Rule:

• If p-value ≤ significance level → Reject null hypothesis
• If p-value > significance level → Fail to reject null hypothesis

Since 1.4% > 1%, the candidate would fail to reject the null hypothesis.

Why Other Options Are Incorrect:

A. Reject the null hypothesis - Incorrect because p-value (1.4%) > significance level (1%)

C. Conclude that the mean return is different from zero - Incorrect because failing to reject the null hypothesis means we don't have sufficient evidence to conclude the mean is different from zero

D. Conclude that the mean return is negative - Incorrect because the test only determines if the mean is different from zero, not the direction of the difference

Key Takeaway:

A p-value greater than the significance level indicates insufficient evidence to reject the null hypothesis, meaning we cannot conclude the mean return is statistically different from zero.