Explanation

In hypothesis testing:

• p-value = 1.4% (0.014)
• Significance level (α) = 1% (0.01)

The decision rule is:

• If p-value ≤ α → Reject the null hypothesis
• If p-value > α → Fail to reject the null hypothesis

Here: 1.4% > 1% (0.014 > 0.01), so we fail to reject the null hypothesis.

Key Points:

1. The p-value (1.4%) represents the probability of observing the test results (or more extreme results) assuming the null hypothesis is true.
2. The significance level (1%) is the threshold we set for rejecting the null hypothesis.
3. Since the p-value is greater than the significance level, we don't have sufficient evidence to reject the null hypothesis.
4. The null hypothesis in this case would be: "The mean return is equal to zero."

Why not the other options:

• A: Incorrect because p-value > α
• C: Incorrect because we fail to reject the null hypothesis, so we cannot conclude the mean is different from zero
• D: Incorrect - we cannot determine the direction (positive or negative) from this test alone; this would require a one-tailed test