Answer: Fail to reject the null hypothesis

## 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

Author: Nikitesh Somanthe