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Statistics
Statistics
1. Introduction to Statistics
2. Data Collection and Sampling
3. Descriptive Statistics: Organizing and Summarizing Data
4. Probability Theory
5. Probability Distributions
6. Sampling Distributions
7. Inferential Statistics: Estimation
8. Inferential Statistics: Hypothesis Testing
9. Analysis of Variance (ANOVA)
10. Correlation and Regression
11. Chi-Square Tests
12. Non-Parametric Statistics
13. Experimental Design
14. Advanced Topics in Statistics
Inferential Statistics: Hypothesis Testing
The Logic of Hypothesis Testing
Scientific Method Connection
Proof by Contradiction
Null Hypothesis (H₀)
Status Quo
No Effect Hypothesis
Alternative Hypothesis (H₁)
Research Hypothesis
What We Want to Prove
One-Tailed vs. Two-Tailed Tests
Directional Hypotheses
Non-Directional Hypotheses
Critical Region Placement
Errors in Hypothesis Testing
Type I Error (α)
False Positive
Consequences
Significance Level
Type II Error (β)
False Negative
Consequences
Factors Affecting Beta
Power of a Test (1 - β)
Factors Affecting Power
Sample Size
Effect Size
Significance Level
Population Variability
Relationship Between Errors
Trade-off Between Type I and Type II
The Hypothesis Testing Process
Stating Hypotheses
Null Hypothesis Formulation
Alternative Hypothesis Formulation
Selecting a Significance Level (α)
Common Levels
Practical Considerations
Identifying the Test Statistic
Appropriate Test Selection
Distribution Assumptions
Formulating a Decision Rule
Critical Value Method
Rejection Region
Calculating the Test Statistic
Formula Application
Computational Steps
Making a Statistical Decision
Critical Value Approach
Comparison with Critical Values
Decision Rules
P-Value Approach
P-Value Calculation
Interpretation
Decision Rules
Interpreting Results
Statistical vs. Practical Significance
Context Considerations
Hypothesis Tests for a Single Population
Test for a Population Mean
Z-test (σ known)
Assumptions
Test Statistic
Steps
t-test (σ unknown)
Assumptions
Test Statistic
Degrees of Freedom
Steps
Test for a Population Proportion (Z-test)
Assumptions
Test Statistic
Sample Size Requirements
Steps
Test for a Population Variance (Chi-Square test)
Assumptions
Test Statistic
Degrees of Freedom
Steps
Hypothesis Tests for Two Populations
Comparing Two Independent Population Means
Equal Variances Assumed
Pooled t-test
Assumptions
Unequal Variances
Welch's t-test
Degrees of Freedom Adjustment
Large Sample Z-test
Comparing Two Dependent Population Means (Paired t-test)
Assumptions
Difference Scores
Test Statistic
Steps
Comparing Two Population Proportions
Independent Samples
Assumptions
Pooled Proportion
Test Statistic
Steps
Comparing Two Population Variances (F-test)
Assumptions
F-statistic
Degrees of Freedom
Steps
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7. Inferential Statistics: Estimation
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9. Analysis of Variance (ANOVA)