Today we started talking about sampling. You can read more about sampling in the textbook. We defined samples, populations, statistics, and parameters. We also talked about sample error: when sample statistics don’t match population parameters. There are two reasons for sample error:
Bias - systematic sources of error
Random error - other non-systematic errors
We tried to pick a random sample of words from the Gettysburg address and saw that most people had biased samples because then tended to pick longer words. We also saw that political polls tend to be biased. The only way to avoid bias in a sample is to pick a simple random sample which is a sample selected randomly from a list of all individuals in the population. Unfortunately, that isn’t always possible.
Today we did this workshop:
Today we talked about how proving cause & effect is hard in science. An explanatory variable is a variable that might be a potential cause and a response variable is the effect. One reason that proving cause and effect is hard is that there are often lurking variables which are variables that were not measured, but they might be a different potential cause.