PSY201H5 Lecture Notes - Lecture 6: Mutual Exclusivity, Binomial Distribution, Statistical Inference
Document Summary
Depends on what"s in the jar: jar= population, marbles= sample (n=2) Inferential statistics reverses this process- we never see the jar. To infer the population: we take a guess at the population parameter by drawing a representative sample, using this sample, we use probability to infer the population. Sample selected in a way that ensured every member of the sample had an equal chance of being picked. Each sample had equal shot of being picked. Each member of population had equal shot. Their probability of getting picked is the same across trials. Why is randomness important: inferences about population requires randomness, more representative on average (some flukes happen, example of non-random. Election poll: small, biased sample that excluded conservative panhandle. Likelihood that an event will occur out of all possible outcomes. P(a)= number of events a/ total number of possible events. Ranges from 0. 00 to 1. 00: 0 (cid:3409) p(a) (cid:3409) 1.
