How can researchers ensure appropriate statistical tests are chosen for small-sample rodent data?

• A. Always use parametric tests regardless of distribution.
• B. Always use non-parametric tests regardless of distribution.
• C. Check normality, homogeneity of variance, and data distribution; use parametric tests if assumptions are met or non-parametric alternatives when not; consult a statistician.
• D. Ignore distribution and choose based on prior literature.

Answer: (C)

When you’re working with small-sample rodent data, choose a statistical test by testing the data’s assumptions first. Parametric tests rely on the sampling distribution being roughly normal and on similar variances between groups. With small samples, these assumptions are easier to violate or harder to verify, so you start by checking normality and homogeneity of variance and looking at the overall data distribution.

If the data look approximately normal and the variances are similar, parametric tests are appropriate because they use more information from the data and generally have higher power, even with small samples. If normality or equal-variance assumptions aren’t met, non-parametric alternatives are safer because they don’t depend on those specific distributional assumptions, though they can be less powerful when the data are truly normal.

For very small samples or when uncertainty remains about the distribution, consider resampling approaches or consult a statistician to choose the best path. Also make sure the test matches your study design (paired measurements vs. independent groups, repeated measures, etc.). This approach—checking normality, variance, and data distribution, then applying parametric tests if assumptions hold or non-parametric ones if they don’t, with expert input as needed—best guides appropriate test selection for small-sample rodent data.