ECO220Y1 Lecture Notes - Lecture 1: Statistical Inference, Sampling Error, White Noise
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ECO220Y1 Full Course Notes
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Population set of all items in interest (ex: all uoft alumni, n = 500k+) Parameter number describing a population (ex: mean income of all uoft alumni) Sample subset of the population (ex: n = random sample of 50 alumni) Statistics number describing a sample (ex: mean income of our sample of 50 uoft alumni) (different from parameter) Sampling error purely random difference between a sample and the population that arises because the sample is a random subset of the population. As sample size gets larger, the sampling error tends to get smaller. Example: sample 3 people in class, average is 7, class average may be 8, difference is called sampling error. Inferential statistics: make inference about a population and its parameters using data (use our information that we can see and we make an inference of the entire population we cannot observe)
