When to Use Parametric vs Nonparametric Tests

TL;DR

Parametric tests require specific population assumptions, while nonparametric tests apply when those assumptions aren't met. Key differences include the type of data distribution, sample size, and measurement scales necessary for analysis.

Transcript

hello and welcome to lesson 32 parametric versus nonparametric tests in this lesson we will try to cover the Bas Concepts about parametric and nonparametric tests and when to use each tests that means when to use parametric test and when to use nonparametric test and lastly we will see some of the parametric tests along with their counterpart non p... Read More

Key Insights

• 😒 Parametric tests use population parameters and specific assumptions.
• 🏆 Nonparametric tests are used when assumptions for parametric tests cannot be met.
• 🪡 Differences include data distribution, sample size, measurement scale, and the need for population knowledge.
• 🥳 Parametric tests require normal distribution, random sampling, large sample sizes, and interval/ratio scales.
• 💦 Nonparametric tests work with any distribution, small sample sizes, nominal/ordinal scales, and no population knowledge.
• 🏆 Decision between parametric and nonparametric tests depends on meeting assumptions and knowing population parameters.
• 🏆 Parametric tests like T Test and ANOVA have nonparametric counterparts like Wilcoxon and Mann-Whitney.

Questions & Answers

Q: What are parametric tests used for?

Parametric tests use population parameters and specific assumptions to make inferences about the population based on a sample drawn from it. They require knowledge of the population.

Q: When should nonparametric tests be used?

Nonparametric tests are used when assumptions for parametric tests cannot be met or when there is no knowledge of population parameters. They are more suitable for skewed data or small sample sizes.

Q: What are the key differences between parametric and nonparametric tests?

Parametric tests require normal distribution, random sampling, large sample sizes, interval/ratio scales, and population knowledge. Nonparametric tests work with any distribution, small sample sizes, nominal/ordinal scales, and no need for population knowledge.

Q: How do you decide between parametric and nonparametric tests?

By checking assumptions like data distribution, sample size, randomness, measurement scale, and population knowledge, researchers can determine whether to use parametric tests or their nonparametric counterparts.

Summary & Key Takeaways

• Parametric tests use population parameters to make inferences based on specific assumptions.

• Nonparametric tests are used when there is no knowledge of population parameters and assumptions can't be met.

• Key differences include data distribution, sample size, measurement scale, and the need for population knowledge.