Final answer:
To determine if there is a significant difference between an accepted value and experimental data, a t-Test is used. This test is suitable when the population standard deviation is unknown and is particularly useful with small sample sizes. The test result depends on the calculated t-score and the corresponding p-value.
Step-by-step explanation:
To determine if there is a statistically significant difference between the accepted value (2.50%) and the average experimental data, the appropriate equation to use would be the t-Test. Option D) t-Test is correct because it is designed to compare means of two groups to find out if they are significantly different from each other. When the population standard deviation is unknown and the sample size is small, the t-Test is particularly useful.
In cases where we have experimental data and an accepted value, we often set up null and alternative hypotheses where the null hypothesis suggests that there is no statistically significant difference between the experimental mean and the accepted value. We then use the t-Test to calculate the t-score, which we can compare with critical t-values from the Student's t-distribution or find the p-value using statistical software to determine if we should reject the null hypothesis. The Student's t-table or statistical software can aid in finding the p-value corresponding to the calculated t-score, given the degrees of freedom (which in turn depend on the sample size).
As an example, if a student calculated a t-score that resulted in a p-value less than the significance level (alpha = 0.05), they would reject the null hypothesis, indicating that there is sufficient evidence to conclude a significant difference exists.