PARAMETRIC AND NON-PARAMETRIC TESTS

Parametric Tests

Assume your data follows a specific distribution — usually a normal distribution (bell-shaped curve).

Key assumptions:

The data is normally distributed
The sample size is large enough
Data is measured on interva…


This content originally appeared on DEV Community and was authored by Njeri Kimaru

Parametric Tests

Assume your data follows a specific distribution — usually a normal distribution (bell-shaped curve).

Key assumptions:

  • The data is normally distributed

  • The sample size is large enough

  • Data is measured on interval or ratio scale

  • Homogeneity of variance (similar spread in groups)

Examples:

  • t-test -- Compare means between 2 groups
  • ANOVA -- Compare means across 3+ groups
  • Pearson correlation -- Relationship between two variables
  • Linear regression -- Predicting outcomes based on predictors

Non-Parametric Tests

Don’t assume any specific distribution of data. These are more flexible, especially for:

  • Skewed data

  • Ordinal data

  • Small sample sizes

Examples:

  • Mann–Whitney U test -- Non-parametric alternative to t-test
  • Kruskal–Wallis test -- Alternative to ANOVA
  • Wilcoxon signed-rank -- Paired samples (like paired t-test)
  • Spearman correlation -- Non-parametric correlation
  • Chi-square test Categorical data (e.g., frequencies)


This content originally appeared on DEV Community and was authored by Njeri Kimaru


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