Why would researchers use parametric tests over non-parametric tests?

Why would researchers use parametric tests over non-parametric tests?

• A. They are more powerful, allowing smaller differences or smaller samples to be detected
• B. They require no assumptions
• C. They are always valid for skewed data
• D. They disregard variance

Answer: (A)

Parametric tests have more power when their assumptions hold. They rely on a specific population distribution (usually normal) and use parameters like the mean and variance to model the data. Because they make these efficient use of information, they’re more capable of detecting real differences or effects, even with smaller differences or smaller sample sizes. If the data meet the assumptions (normality, homogeneity of variance, independence, appropriate measurement scale), this power advantage makes parametric tests the better choice. When data are skewed or violate these assumptions, the power advantage diminishes or disappears, which is why nonparametric methods are often preferred in those cases. Parametric tests do not disregard variance; rather, they incorporate it into the test statistic. They also do require assumptions about distribution, so they aren’t automatically valid for all data.

Parametric tests have more power when their assumptions hold. They rely on a specific population distribution (usually normal) and use parameters like the mean and variance to model the data. Because they make these efficient use of information, they’re more capable of detecting real differences or effects, even with smaller differences or smaller sample sizes. If the data meet the assumptions (normality, homogeneity of variance, independence, appropriate measurement scale), this power advantage makes parametric tests the better choice. When data are skewed or violate these assumptions, the power advantage diminishes or disappears, which is why nonparametric methods are often preferred in those cases. Parametric tests do not disregard variance; rather, they incorporate it into the test statistic. They also do require assumptions about distribution, so they aren’t automatically valid for all data.