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Data Science

399When to Use the One-Sample Z-Test for Proportions
400Assumptions and Conditions for Proportion Tests
401Setting Up Hypotheses for Proportions
402Calculating the Test Statistic for Proportions
403Finding P-Values for Proportion Tests
404Making Decisions and Drawing Conclusions
405Sample Size and Power for Proportion Tests
406Two-Sample Proportion Test Setup
407Pooled Proportion Estimator
408Standard Error for Difference in Proportions
409Z-Test Statistic for Two Proportions
410P-Value Calculation and Interpretation
411Sample Size Requirements
412Confidence Interval for Difference
413Effect Size and Practical Significance
414Introduction to Chi-Squared Goodness of Fit Test
415Setting Up Hypotheses for Goodness of Fit
416Calculating Expected Frequencies
417The Chi-Squared Test Statistic Formula
418Degrees of Freedom in Goodness of Fit
419Assumptions and Minimum Expected Frequencies
420Interpreting Chi-Squared Test Results
421Applications: Uniform, Genetic Ratios, and Distributions
422Introduction to Chi-Squared Test of Independence
423Contingency Tables and Expected Frequencies
424The Chi-Squared Test Statistic
425Degrees of Freedom for Independence Tests
426Assumptions and Sample Size Requirements
427Interpreting Chi-Squared Test Results
428Post-Hoc Analysis and Residuals
429Effect Size: Cramér's V and Phi
430Common Applications and Pitfalls
431When Chi-Squared Assumptions Fail
432Fisher's Exact Test: The Logic
433Conducting Fisher's Exact Test
434Fisher's Exact vs Chi-Squared: When to Use Each
435McNemar's Test: Paired Categorical Data
436Conducting McNemar's Test
437Applications: Clinical Trials and Market Research

Data Science

399When to Use the One-Sample Z-Test for Proportions
400Assumptions and Conditions for Proportion Tests
401Setting Up Hypotheses for Proportions
402Calculating the Test Statistic for Proportions
403Finding P-Values for Proportion Tests
404Making Decisions and Drawing Conclusions
405Sample Size and Power for Proportion Tests
406Two-Sample Proportion Test Setup
407Pooled Proportion Estimator
408Standard Error for Difference in Proportions
409Z-Test Statistic for Two Proportions
410P-Value Calculation and Interpretation
411Sample Size Requirements
412Confidence Interval for Difference
413Effect Size and Practical Significance
414Introduction to Chi-Squared Goodness of Fit Test
415Setting Up Hypotheses for Goodness of Fit
416Calculating Expected Frequencies
417The Chi-Squared Test Statistic Formula
418Degrees of Freedom in Goodness of Fit
419Assumptions and Minimum Expected Frequencies
420Interpreting Chi-Squared Test Results
421Applications: Uniform, Genetic Ratios, and Distributions
422Introduction to Chi-Squared Test of Independence
423Contingency Tables and Expected Frequencies
424The Chi-Squared Test Statistic
425Degrees of Freedom for Independence Tests
426Assumptions and Sample Size Requirements
427Interpreting Chi-Squared Test Results
428Post-Hoc Analysis and Residuals
429Effect Size: Cramér's V and Phi
430Common Applications and Pitfalls
431When Chi-Squared Assumptions Fail
432Fisher's Exact Test: The Logic
433Conducting Fisher's Exact Test
434Fisher's Exact vs Chi-Squared: When to Use Each
435McNemar's Test: Paired Categorical Data
436Conducting McNemar's Test
437Applications: Clinical Trials and Market Research

← Data Science

Lesson 435 of 2,145·11. Hypothesis Tests for Proportions and CategoriesPro lesson

McNemar's Test: Paired Categorical Data

Understand when to use McNemar's Test for analyzing matched pairs or repeated measures with binary outcomes.

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