The degrees of freedom (df) in a one-sample t-test is calculated as:

\[
\text{Degrees of Freedom} = n - 1
\]

Where:
- \(n\) is the sample size.

Given that the sample size \(n = 15\):

\[
\text{Degrees of Freedom} = 15 - 1 = 14
\]

So, the degrees of freedom for this test is **14**.

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**Do you want details or have any questions?**

Here are some relative questions to consider:

1. How do degrees of freedom affect the shape of the t-distribution?
2. What happens to the t-distribution as the degrees of freedom increase?
3. Why are degrees of freedom important in statistical testing?
4. How do you calculate degrees of freedom in a two-sample t-test?
5. How does the degrees of freedom affect the critical t-value?

**Tip:** In hypothesis testing, the degrees of freedom are crucial for determining the appropriate critical value from the t-distribution, which directly influences the p-value and conclusion.

Learn about degrees of freedom in statistical testing, specifically in a one-sample t-test scenario. Discover how to calculate degrees of freedom using the formula df = n - 1, with a practical example where n = 15. Understand the significance of degrees of freedom in determining critical values from the t-distribution and interpreting p-values.

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