|Main Concepts | Demonstration | Activity | Teaching Tips | Data Collection & Analysis | Practice Questions | Milestone | Fathom Tutorial|
• By the end of the course, students will be expected 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.
• Randomization 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 randomization eliminates confounding effects rather than balancing treatment groups with regard to those effects.
• 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.
• Researchers try to control for every conceivable confounding variable, but in practice there is no way to guarantee that every existing confounding variable has been considered and controlled.
• "Bias" is a complicated term, and will be refined in Unit 9.
• 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
Blocking and Stratifying (which we'll address in Unit 15)
• Teaching blocking is hard, in part because the curriculum
doesn't cover analysis of block designs and so students never see the
payoff from this technique.
• Students will confuse blocking and stratification. They have
similar purposes but arise in different contexts, and students need
experience in both.