Do parametric tests require underlying assumptions to be met?

Do parametric tests require underlying assumptions to be met?

• A. True
• B. False
• C. Only sometimes
• D. It depends on sample size.

Answer: (A)

Parametric tests are built on a defined statistical model for the data, so they rely on specific conditions about the population and the data collection. This means there are key assumptions, such as the outcome being measured on an interval or ratio scale, observations being independent, and the population distributions (for the groups being compared) being approximately normal, with equal variances across groups for many tests. When these assumptions hold, the test statistics have known sampling distributions, which lets us trust the p-values and confidence intervals produced. If these assumptions are violated, the results can be biased or misleading, which is why researchers check assumptions and may use data transformations, robust methods, or nonparametric tests that require fewer assumptions. While larger samples can mitigate some issues through the central limit theorem, this doesn’t completely remove the need for the underlying assumptions. So parametric tests do require underlying assumptions to be met.

Parametric tests are built on a defined statistical model for the data, so they rely on specific conditions about the population and the data collection. This means there are key assumptions, such as the outcome being measured on an interval or ratio scale, observations being independent, and the population distributions (for the groups being compared) being approximately normal, with equal variances across groups for many tests. When these assumptions hold, the test statistics have known sampling distributions, which lets us trust the p-values and confidence intervals produced.

If these assumptions are violated, the results can be biased or misleading, which is why researchers check assumptions and may use data transformations, robust methods, or nonparametric tests that require fewer assumptions. While larger samples can mitigate some issues through the central limit theorem, this doesn’t completely remove the need for the underlying assumptions. So parametric tests do require underlying assumptions to be met.