Unit 4: Collecting Data Main Concepts | Demonstration | Teaching Tips | Data Analysis & Activity | Practice Questions | Connections | Fathom Tutorial | Milestone
 Teaching Tips • By the end of the course, students should be able to design an experiment and explain their design clearly in writing. • Don't get hung up on the difference between "lurking variables" and "confounding variables". It just doesn't matter. • It's useful for some to think of the process of comparing groups to be that of distinguishing the signal (treatment effect) from the noise (of variation). Collecting more data helps the signal stand out from the noise. • "Bias" is a complicated term, and will be refined in Unit 9. • Students need to understand and be able to identify potential sources of bias. Don't get caught up in distinguishing among the different names. • All of the main and important concepts about sampling can be understood in the context of simple random sampling. The other types of sampling are technical implementations, but do not affect the main concepts. • Sampling with and without replacement: in practice we virtually always sample without replacement, but in classroom situations you might sample with replacement, which simulates sampling without replacement from a very large population. Student Misconceptions and Confusions • Randomization or random assignment is not meant to make the treatment groups *exactly* equal after the fact, but is, in a way we'll understand after studying probability, meant to help us quantify the reliability of our answers. • Students mistakenly believe that random assignment eliminates confounding effects rather than balancing treatment groups with regard to those effects. Resources • Table with random sample vs. assignment