Parametric statistics are typically associated with which types of measurements?

• A. Nominal data
• B. Interval or ratio data
• C. Ordinal data
• D. Dichotomous data

Answer: (B)

Parametric statistics rely on data that allow meaningful arithmetic operations and on distributional assumptions about the population. The measurement scales that provide these properties are interval and ratio data.

Interval data have equal distances between values, like temperature intervals, but do not have a true zero. Ratio data add a true zero, so operations like ratios are meaningful, as with height, weight, or time. Because these scales support calculating means, variances, and applying models (such as t-tests, ANOVA, or regression) under assumptions like normality and homogeneity of variance, they are the typical targets of parametric methods.

By contrast, nominal data are categories without inherent order, and ordinal data have order but unequal intervals; these scales do not support the same arithmetic and distributional assumptions, so nonparametric methods are usually used instead unless data can be transformed or particular conditions apply.