 A repeated-measures study* using a sample of *n* = 20 participants would produce a *t* statistic with *df* = _____.

In a repeated-measures study, the degrees of freedom (\(df\)) for the t-test are calculated based on the number of participants. The formula for the degrees of freedom in this case is:

\[
df = n - 1
\]

Where \(n\) is the number of participants.

Given:
- \(n = 20\)

Thus, the degrees of freedom (\(df\)) would be:

\[
df = 20 - 1 = 19
\]

So, the \(df\) would be 19.

Would you like further explanation or have any other questions?

Here are some related questions you might consider:
1. How do degrees of freedom affect the critical value in a t-test?
2. What is a repeated-measures study, and why is it used?
3. How does a repeated-measures design differ from an independent-measures design?
4. What assumptions are necessary for a repeated-measures t-test?
5. How do you interpret the t-statistic in a repeated-measures study?
6. What are the advantages and disadvantages of using a repeated-measures design?
7. How would you calculate the effect size for a repeated-measures study?
8. How does sample size impact the power of a repeated-measures t-test?

**Tip:** In a repeated-measures study, since the same participants are measured more than once, individual differences are controlled for, often leading to a more powerful test.

Understand how to calculate degrees of freedom (df) in a repeated-measures study with n = 20 participants. Explore the formula df = n - 1 and its application in statistical analysis. Suitable for advanced undergraduate students and researchers.

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