Non-parametric statistics are typically used when data are measured on which scales?

Non-parametric statistics are typically used when data are measured on which scales?

• A. Nominal or Ordinal
• B. Interval
• C. Ratio
• D. All Scales

Answer: (A)

Non-parametric statistics don’t rely on a specific distribution and don’t assume equal intervals between measurement points. This makes them well suited for data on nominal or ordinal scales, where you can categorize or rank observations but can’t rely on precise differences or meaningful means. With nominal data, you’re often looking at counts or frequencies and associations between categories (for example, a chi-square test). With ordinal data, you can rank order the observations but the spacing between ranks isn’t guaranteed to be equal, so rank-based methods (like Mann-Whitney or Kruskal-Wallis) are used rather than mean comparisons. Parametric tests require interval or ratio data with roughly normal distributions, so they’re less appropriate when the data are nominal or ordinal. Non-parametric methods can also be used when interval/ratio data violate assumptions, but the typical scenario driving their use is nominal or ordinal data.

Non-parametric statistics don’t rely on a specific distribution and don’t assume equal intervals between measurement points. This makes them well suited for data on nominal or ordinal scales, where you can categorize or rank observations but can’t rely on precise differences or meaningful means.

With nominal data, you’re often looking at counts or frequencies and associations between categories (for example, a chi-square test). With ordinal data, you can rank order the observations but the spacing between ranks isn’t guaranteed to be equal, so rank-based methods (like Mann-Whitney or Kruskal-Wallis) are used rather than mean comparisons. Parametric tests require interval or ratio data with roughly normal distributions, so they’re less appropriate when the data are nominal or ordinal. Non-parametric methods can also be used when interval/ratio data violate assumptions, but the typical scenario driving their use is nominal or ordinal data.