POL101Y1 Lecture Notes - Institute For Operations Research And The Management Sciences, Statistic, Normal Distribution
11 Dec 2012
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Document Summary
Today focused on how well a sample statistic reflects a population. Rids of systematic error, but not sampling error. Sometimes when you randomly pick, you pick a biased result. End product: population statistic = sample statistic +/- amount of sampling error willing to incur. Amount of sampling error will to incur = t(s / square root of n) Cannot just say your data is exactly what the real population feels. Interval level variable (rate him on a scale) Some people don"t fill out the survey properly, some don"t show up etc. We assume this still represents the whole pop. Normally we don"t know how canadians would feel about mulcair. Here we pretend we have all the pop data. Need to create a basket of confidence (or allowable error) around sample mean. 2 components: 1. variance and 2. sample size: variance: spread of data around statistic. Why? b/c less variance in pop, less likelihood sample will be of extreme cases.
