Hypothesis Testing Hub

This is the main hub for hypothesis testing topics. The content has been organized into several focused files for better navigation and understanding.

Core Concepts

1. Hypothesis Testing Basics

   • Introduction to hypothesis testing
   • Core components and hypotheses
   • General outline of the testing process
   • Court case analogy for understanding
   • Examples of hypothesis testing questions

2. Hypothesis Testing Key Concepts

   • Null distribution
   • Test statistics
   • P-values and their interpretation
   • Significance levels
   • Type I and Type II errors

3. Hypothesis Testing Procedure

   • Step-by-step process
   • Data collection and conditions
   • Test statistic calculation
   • P-value determination
   • Decision making process

4. Types of Hypothesis Tests

   • Test selection framework
   • Tests for proportions
   • Tests for means
   • Decision guide
   • Best practices

Specific Tests

5. Hypothesis Testing for Proportions

   • Single population proportion tests
   • Difference of two population proportions tests
   • Conditions for valid tests
   • Z-test for proportions

6. Hypothesis Testing for Means

   • Single population mean tests
   • Difference of two population means tests
   • T-tests and Z-tests
   • Paired and independent samples

7. Chi-Square Tests

   • Chi-square tests for independence
   • Chi-square tests for goodness of fit
   • Contingency tables
   • Expected counts

8. ANOVA

   • One-way ANOVA
   • Multiple comparison tests
   • F-distribution
   • Assumptions and conditions

Decision Making and Errors

9. Decision Making

   • Decision making process
   • P-value and decision making
   • Statistical vs practical significance
   • Strength of evidence approach

10. Type I and Type II Errors

    • Understanding error types
    • Error probabilities
    • Power of a test
    • Sample size considerations

Related Topics

• Central Limit Theorem - Understanding the theoretical foundation for hypothesis testing
• Critical Values - Important values for making decisions in hypothesis testing
• Confidence Interval - Alternative approach to statistical inference
• Standard error - Understanding the denominator in test statistics
• degrees of freedom - Important concept for t-distribution tests
• sampling distribution - Foundation for understanding null distributions
• Normal Distribution - Common distribution for test statistics
• t-distribution - Used in hypothesis testing for means when population standard deviation is unknown
• Bootstrap - Alternative approach to hypothesis testing using resampling
• Statistical Significance - Understanding what makes results significant
• Variance - Important concept in hypothesis testing
• Independence - Key assumption in many statistical tests
• Power Analysis - Planning for adequate sample size
• Multiple Comparisons - Handling multiple tests
• P-hacking - Understanding and avoiding data manipulation
• Effect Size - Measuring practical importance
• Sample Size - Impact on test power and error rates