BiteSizedChunks.comLearn one small thing at a time.

Course contentsShow

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

Effect Size and Practical Significance

Interpreting magnitude of proportion differences beyond statistical significance.

This lesson is for subscribers

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

See pricing Back to course