Videos: Random error – the motivation for inference [7 min, 6 min]
Random error in estimates is the problem we try to solve using formal methods of inference. Confidence intervals, e.g. those obtained from bootstrap resampling, are a solution.
In these 2 videos, we focus on the errors we make when we use data from a sample to tell us something about a whole population. We ask: “How wrong could I be?” We also show the difference between the effects of systematic biases and random errors as we take more and more observations.
(See also the Review Questions following these two movies.)
[Illustrated transcript (pdf) for Part I]
[Illustrated transcript (pdf) for Part I]
After you’ve watched these videos, you should be able to answer these questions:
Part 1 video
- Do the problems caused by bad measurement systems and biased selection mechanisms go away when we get huge amounts of data?
- Do the problems caused by confounding go away when we get huge amounts of data?
- Do the problems caused by random error go away when we get huge amounts of data?
- What is a sampling error?
- Do we ever know how big the actual sampling error we incurred was? What do we try to do about that?
Part 2 video
- What effect does sample size have on sampling error?
- For what two reasons are non-random selection mechanisms worse than random selection mechanisms?
- What were the 5 “take home messages” from this movie?
If you couldn’t answer a question, you might find it helpful to look at the illustrated transcripts (linked under each movie).
