Unit 10: Confidence Intervals Main Concepts | Demonstration | Teaching Tips | Data Analysis & Activity | Practice Questions | Connections | Fathom Tutorial | Milestone
 Connections • Exploratory Data Analysis Confidence intervals and inference help students use data to just answer that simple investigative question that they may have developed about a particular population. This should reference back to the first unit of the course. In order to evaluate whether assumptions hold, need to look at shape, center, spread of sample distribution. • Inference Confidence intervals help us make inference about a population using just a single sample. Just as the last unit discussed, it is important to reiterate that we just need one sample to make inference. Confidence intervals rely on sampling distributions and the Central Limit Theorem (if assumptions are met), so it is important to show students how these concepts all relate. For assumptions to be met, we need to follow good data collection practices to obtain a random sample. If the Central Limit Theorem applies, students will need to recall normal models and z-scores. Remember: their sample may not be normal, but the sampling distribution of their sample mean or proportion is normal under Central Limit Theorem.