To determine the meaning of the p-value in the context of the study given:
1. Understand the p-value: The p-value is a measure that helps determine the significance of the test results. It represents the probability of obtaining test results at least as extreme as those observed, under the assumption that the null hypothesis is true.
2. Compare the p-value to the significance level (alpha): The p-value is given as 0.0043, and the significance level (alpha) is given as 0.05.
3. Decision Rule: If the p-value is less than the significance level (alpha), we reject the null hypothesis. If the p-value is greater than or equal to alpha, we fail to reject the null hypothesis.
4. Interpret the p-value: Given the p-value (0.0043) is less than alpha (0.05), we reject the null hypothesis. This means the observed test result is statistically significant.
5. Explanation of the p-value: The p-value of 0.0043 indicates that there is a 0.43% chance of observing the test results, or results more extreme, due to random variation alone, assuming the null hypothesis is true.
Therefore, the correct interpretation of the p-value is:
OD) It means there is less than a 0.43 percent chance that the test statistic represents normal variability.
In conclusion: The p-value of 0.0043 being less than the significance level (0.05) implies that the test result is statistically significant, and there is a very low probability that the observed test statistic occurred due to random chance under the null hypothesis.
