Good statistical inference depends on high-quality data collection. In this unit, students transition from analyzing data to understanding its origins by learning how to select random samples and design well-controlled experiments.

Students will explore various sampling methods, including Simple Random Sampling (SRS), stratified, cluster, and systematic sampling, while identifying potential sources of bias such as nonresponse, undercoverage, and response bias.

The curriculum emphasizes the four principles of experimental design: comparison, random assignment, replication, and control. Students will learn to distinguish between observational studies and experiments, identify confounding variables, and explain how random assignment allows for causation while random selection allows for generalization to a population. Finally, students will use simulations to estimate probabilities and model real-world variability.