1. What is the Dependent T Test?

The dependent t-test (also known as paired t-test) is used to compare the means of two related groups or measurements. It determines whether there is a statistically significant difference between the means of paired observations.

2. How Does the Calculator Work?

The calculator uses the dependent t-test formula:

Where:

• \( \text{mean\_diff} \) — Mean of differences between paired observations
• \( \text{sd\_diff} \) — Standard deviation of the differences
• \( n \) — Number of pairs
• \( t \) — T-statistic value

Explanation: The formula calculates how many standard errors the mean difference is from zero, indicating whether the observed difference is statistically significant.

3. Importance of T-Statistic Calculation

Details: The t-statistic is crucial for hypothesis testing in paired experimental designs. It helps determine if treatment effects, pre-post differences, or other paired comparisons are statistically significant.

4. Using the Calculator

Tips: Enter the mean of differences, standard deviation of differences, and number of pairs. All values must be valid (n ≥ 1, sd_diff ≥ 0).

5. Frequently Asked Questions (FAQ)

Q1: When should I use a dependent t-test?
A: Use it when you have paired or matched observations, such as pre-test/post-test measurements, matched case-control studies, or repeated measurements on the same subjects.

Q2: What assumptions does this test make?
A: The test assumes that the differences are normally distributed, the observations are paired, and the differences have constant variance.

Q3: How do I interpret the t-value?
A: Compare the calculated t-value to critical values from the t-distribution table with n-1 degrees of freedom. A larger absolute t-value indicates stronger evidence against the null hypothesis.

Q4: What's the difference between dependent and independent t-tests?
A: Dependent t-test compares means from the same subjects under different conditions, while independent t-test compares means from different groups of subjects.

Q5: What if my data violates the normality assumption?
A: For non-normal difference scores, consider using non-parametric alternatives like the Wilcoxon signed-rank test.