PARAMETRIC AND NON-PARAMETRIC TESTS

PARAMETRIC AND NON-PARAMETRIC TESTS

PARAMETRIC TESTS

🔹 DEFINITION
Parametric tests are statistical tests that assume the underlying data follows a known distribution, usually a normal distribution.

🔹 CHARACTERISTICS

• Based on interval or ratio scale data

• Assume normality and equal variances

• Require quantitative data

• More powerful when assumptions are met

🔹 EXAMPLES

• t-Test (for comparing means)

• z-Test (for large sample mean comparisons)

• ANOVA (Analysis of Variance)

• Pearson’s correlation coefficient

🔹 WHEN TO USE

• When data is normally distributed

• Sample size is large

• Measurement scale is interval or ratio

NON-PARAMETRIC TESTS

🔹 DEFINITION
Non-parametric tests are statistical tests that do not require any specific distribution of the data. They are often used for ordinal data or when parametric assumptions are not met.

🔹 CHARACTERISTICS

• Used for nominal or ordinal data

• Do not assume normal distribution

• Less powerful than parametric tests

• Can be used for small sample sizes

🔹 EXAMPLES

• Chi-square test (for association in categorical data)

• Mann–Whitney U test (alternative to independent t-test)

• Wilcoxon signed-rank test (alternative to paired t-test)

• Kruskal–Wallis test (alternative to ANOVA)

• Spearman’s rank correlation

🔹 WHEN TO USE

• Data is not normally distributed

• Sample size is small

• Data is on ordinal or nominal scale

DIFFERENCES BETWEEN PARAMETRIC AND NON-PARAMETRIC TESTS