PY 211 Lecture Notes - Lecture 9: Bias Of An Estimator, Central Limit Theorem, Variance
23 Feb 2017
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Random sampling: every item in pool has equal chance of selection, the probability of remaining selections cannot change after selection (conditional probability) In theory: the order in which items are sampled matters, sampling with replacement. In practice: the order in which items are sampled does not matter, sampling without replacement. Definition of sampling distribution: a distribution of all possible sample means that could be obtained , in sample of a given size, from the same population. The mean of the sampling distribution: symbol: m, m m = , m is an unbiased estimator of , on average, the sample mean will equal the value of , central limit theorem. The average of the sample variances: symbol: s^2, s^2 s^2 = ^2, s^2 is an unbiased estimator of ^2, on average, the sample variances will equal the value of ^2 . Divide population variance by sample size (n = 2)
