formulation of research problem and hypothesis
- 1. Hypothesis Testing • Your Name • Institution • Date
- 2. What is Hypothesis Testing? • Definition and importance in research and decision-making.
- 3. Key Concepts • Population, sample, parameter, statistic. • Null and alternative hypotheses.
- 4. Types of Hypotheses • Null Hypothesis (H₀) • Alternative Hypothesis (H₁) • Directional vs Non-directional.
- 5. Purpose of Hypothesis Testing • To test assumptions using sample data. • Make inferences about population parameters.
- 6. Steps in Hypothesis Testing • 1. State H₀ and H₁ • 2. Choose significance level (α) • 3. Select test statistic • 4. Compute p-value • 5. Make decision
- 7. Significance Level (α) • Common values: 0.05, 0.01. • α is the probability of Type I error.
- 8. Types of Errors • Type I Error (α): Rejecting H₀ when true. • Type II Error (β): Not rejecting H₀ when false.
- 9. One-tailed vs Two-tailed Tests • Use based on hypothesis direction. • Visual of critical regions.
- 10. P-value Approach • Definition of p-value. • Compare with α to draw conclusion.
- 11. Z-Test Overview • Used when population variance is known.
- 12. T-Test Overview • Used when population variance is unknown and sample size is small.
- 13. One-Sample T-Test • Compare sample mean with known value.
- 14. Two-Sample T-Test • Compare means of two independent or paired samples.
- 15. Chi-Square Test • Used for categorical data. • Tests for independence or goodness of fit.
- 16. ANOVA (Analysis of Variance) • Compare means of three or more groups.
- 17. Non-Parametric Tests • Used when normality assumptions are violated.
- 18. Decision Criteria Table • Summary of which test to use in various situations.
- 19. Hypothesis Testing in Business • Example: product effectiveness, A/B testing.
- 20. Hypothesis Testing in Medicine • Example: clinical trials, drug testing.
- 21. Example Problem: Z-Test • Walkthrough of Z-test with data.
- 22. Example Problem: T-Test • Walkthrough of T-test with data.
- 23. Common Misinterpretations • Misunderstanding p-values. • Statistical vs practical significance.
- 24. Ethical Use of Hypothesis Testing • Avoid p-hacking. • Ensure transparency.
- 25. Summary of Key Concepts • Recap steps, errors, test types.
- 26. Advantages of Hypothesis Testing • Objective decision-making. • Wide application.
- 27. Limitations of Hypothesis Testing • Requires assumptions. • May be misused.
- 28. Recent Trends in Hypothesis Testing • Bayesian inference. • AI and ML integration.
- 29. Quiz / Quick Recap • A few recap questions to reinforce learning.
- 30. Thank You / Q&A • Open for questions. • Contact details.