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The least useful factor for determining the appropriateness of parametric versus nonparametric tests is the information that subjects were not randomly selected.

Parametric tests typically rely on certain assumptions about the data, including the distribution of the scores (for instance, whether they are normally distributed), the scale of measurement (such as interval or ratio), and the homogeneity of variance among groups. Nonparametric tests, on the other hand, do not make strong assumptions about data distributions and are often employed when those assumptions cannot be validated.

Knowing if the scores have equal intervals and an absolute zero point is essential because this information confirms that the data are measured on an appropriate scale for parametric testing. Understanding the shape of the distribution, such as being negatively skewed, informs decisions regarding which tests may be more suitable based on distributional assumptions. Additionally, knowing the purpose is to compare observed and expected distributions is relevant for selecting specific statistical tests designed for such comparisons.

However, whether subjects were randomly selected primarily pertains to the generalizability of the results rather than the choice between parametric and nonparametric tests. While random selection can influence the robustness and application of results, it does not inherently determine the type of statistical test one should use based on the characteristics of