Training on Introduction to Statistics 2: Inference and Association

Module 1: Review of Descriptive Statistics and Probability Concepts

• Measures of central tendency and dispersion

• Probability distributions and sampling concepts

• Case Study: Summarizing household income distribution for a social program

Module 2: Sampling Methods and the Central Limit Theorem

• Random, stratified, and cluster sampling

• Understanding the sampling distribution of the mean

• Case Study: Designing an effective sample for a public health survey

Module 3: Introduction to Statistical Inference

• Population parameters vs. sample statistics

• Confidence intervals and margin of error

• Case Study: Estimating average literacy rates across regions

Module 4: Hypothesis Testing Fundamentals

• Null and alternative hypotheses

• Type I and Type II errors

• p-values and statistical significance

• Case Study: Testing whether a training intervention improves performance

Module 5: Comparing Means and Proportions

• t-tests (one-sample, independent, paired)

• z-tests and chi-square for proportions

• Case Study: Assessing the impact of a nutrition program on BMI changes

Module 6: Analysis of Variance (ANOVA)

• One-way and two-way ANOVA

• Post-hoc comparisons and interpretation

• Case Study: Comparing productivity levels across multiple departments

Module 7: Correlation Analysis

• Pearson, Spearman, and Kendall correlation coefficients

• Interpreting correlation strength and direction

• Case Study: Examining the relationship between education and income

Module 8: Simple and Multiple Linear Regression

• Building regression models

• Interpreting coefficients and R²

• Model diagnostics and assumptions

• Case Study: Predicting project outcomes based on budget and staff input

Module 9: Categorical Data and Association Tests

• Chi-square test of independence

• Fisher’s exact test and odds ratios

• Case Study: Analyzing associations between gender and service access

Module 10: Reporting and Interpreting Statistical Results

• Communicating statistical findings clearly

• Visualizing inferential results (confidence intervals, regression plots)

• Case Study: Developing a policy brief from analyzed survey data