To compare the mean blood pressure of the two types of rats using a two-sample $t$-test (Option 2), we need to determine the degrees of freedom. For the conservative approach, the degrees of freedom (df) are determined by the smaller of $n_1 - 1$ and $n_2 - 1$, where $n_1$ and $n_2$ are the sample sizes of the two groups.

Given:

• $n_1 = 18$ (high-capacity rats)
• $n_2 = 18$ (low-capacity rats)

We calculate the degrees of freedom as: $df = \min(n_1 - 1, n_2 - 1) = \min(18 - 1, 18 - 1) = \min(17, 17) = 17$

Thus, the associated degrees of freedom for this comparison using the conservative Option 2 $t$-procedures is 17.

Would you like further details on this method or its application?

Here are 5 related questions that might help expand your understanding:

1. How does the degrees of freedom affect the $t$-distribution in hypothesis testing?
2. Why do we use the conservative approach for degrees of freedom?
3. What assumptions are required for a two-sample $t$-test to be valid?
4. How would the degrees of freedom change if the sample sizes were unequal?
5. Can the $t$-procedures be used if the data is not normally distributed?

Tip: In hypothesis testing with small sample sizes, always check for normality or use non-parametric tests when normality cannot be assumed.