Lecture 2 – Data Sampling and Probability
Presented by Fernando Perez and Suraj Rampure
Content by Fernando Perez, Suraj Rampure, Ani Adhikari, and Joseph Gonzalez
A reminder – the right column of the table below contains Quick Checks. These are required – they are worth 5% of your grade if you are an undergraduate – but are graded on completion, not correctness. A random one of the following six Google Forms will give you an alphanumeric code once you submit; you should take this code and enter it into the “Lecture 2” question in the “Quick Check Codes” assignment on Gradescope to get credit for submitting this Quick Check. You must submit this by Tuesday, September 8th at 11:59PM to get credit for it.
Censuses and surveys. Issues with the US Census.
Samples. Drawbacks to convenience and quota samples.
A case study in sampling bias (1936 election).
Sources of bias, and a formal definition of sampling frames.
Probability samples, and why we need them.
Introducing binomial and multinomial probability calculations.
Generalizing binomial and trinomial probability calculations.
Using permutations and combinations to derive the binomial coefficient.
Example usages of the binomial coefficient.