BiteSizedChunks.comLearn one small thing at a time.

Course contentsShow

Data Science

351When to Use a One-Sample t-Test
352The t-Distribution and Degrees of Freedom
353Calculating the t-Statistic
354Setting Up Hypotheses for One-Sample t-Test
355Finding Critical Values and P-Values
356Making Decisions and Stating Conclusions
357Confidence Intervals and t-Tests
358Worked Example: One-Sample t-Test in Practice
359Two-Sample t-Test Overview
360Independent vs. Dependent Samples
361Pooled Variance t-Test
362Welch's t-Test for Unequal Variances
363Testing Equality of Variances
364Degrees of Freedom in Two-Sample Tests
365Conducting Two-Sample t-Tests by Hand
366Two-Sample t-Tests in Python and R
367Interpreting Two-Sample Test Results
368Common Pitfalls and Best Practices
369When to Use a Paired t-Test
370Differences as the Unit of Analysis
371Calculating Paired Differences
372Paired t-Test Statistic and Degrees of Freedom
373Hypotheses for Paired t-Tests
374Assumptions of the Paired t-Test
375Paired t-Test vs Two-Sample t-Test
376The Assumption of Normality in t-Tests
377Testing Normality: Visual Methods
378Testing Normality: Statistical Tests
379The Assumption of Equal Variances (Homoscedasticity)
380Testing Equal Variances: Levene's and Bartlett's Tests
381Independence Assumption and Its Violations
382Robustness of t-Tests to Assumption Violations
383Diagnostic Workflow: When to Proceed or Switch Tests
384What is Effect Size?
385Cohen's d for Standardized Mean Differences
386Effect Size Interpretation Guidelines
387Confidence Intervals for Effect Sizes
388Effect Size in Sample Size Planning
389Reporting Effect Sizes in Practice
390When Parametric Tests Fail: Violations of Assumptions
391The Sign Test for Medians
392Wilcoxon Signed-Rank Test
393Mann-Whitney U Test (Wilcoxon Rank-Sum)
394Interpreting Rank-Based Tests: Medians vs Distributions
395Permutation Tests for Means and Beyond
396Bootstrap Hypothesis Testing
397Power and Efficiency of Non-Parametric Tests
398Choosing Between Parametric and Non-Parametric Tests

Data Science

351When to Use a One-Sample t-Test
352The t-Distribution and Degrees of Freedom
353Calculating the t-Statistic
354Setting Up Hypotheses for One-Sample t-Test
355Finding Critical Values and P-Values
356Making Decisions and Stating Conclusions
357Confidence Intervals and t-Tests
358Worked Example: One-Sample t-Test in Practice
359Two-Sample t-Test Overview
360Independent vs. Dependent Samples
361Pooled Variance t-Test
362Welch's t-Test for Unequal Variances
363Testing Equality of Variances
364Degrees of Freedom in Two-Sample Tests
365Conducting Two-Sample t-Tests by Hand
366Two-Sample t-Tests in Python and R
367Interpreting Two-Sample Test Results
368Common Pitfalls and Best Practices
369When to Use a Paired t-Test
370Differences as the Unit of Analysis
371Calculating Paired Differences
372Paired t-Test Statistic and Degrees of Freedom
373Hypotheses for Paired t-Tests
374Assumptions of the Paired t-Test
375Paired t-Test vs Two-Sample t-Test
376The Assumption of Normality in t-Tests
377Testing Normality: Visual Methods
378Testing Normality: Statistical Tests
379The Assumption of Equal Variances (Homoscedasticity)
380Testing Equal Variances: Levene's and Bartlett's Tests
381Independence Assumption and Its Violations
382Robustness of t-Tests to Assumption Violations
383Diagnostic Workflow: When to Proceed or Switch Tests
384What is Effect Size?
385Cohen's d for Standardized Mean Differences
386Effect Size Interpretation Guidelines
387Confidence Intervals for Effect Sizes
388Effect Size in Sample Size Planning
389Reporting Effect Sizes in Practice
390When Parametric Tests Fail: Violations of Assumptions
391The Sign Test for Medians
392Wilcoxon Signed-Rank Test
393Mann-Whitney U Test (Wilcoxon Rank-Sum)
394Interpreting Rank-Based Tests: Medians vs Distributions
395Permutation Tests for Means and Beyond
396Bootstrap Hypothesis Testing
397Power and Efficiency of Non-Parametric Tests
398Choosing Between Parametric and Non-Parametric Tests

← Data Science

Lesson 377 of 2,145·10. Hypothesis Tests for MeansPro lesson

Testing Normality: Visual Methods

Using histograms, Q-Q plots, and density plots to assess whether your data meets normality assumptions.

This lesson is for subscribers

You've completed the free preview. Subscribe to unlock every lesson in every course.

See pricing Back to course