PSYC 3000 Lecture Notes - Lecture 12: Confidence Interval, Sampling Distribution, Statistical Hypothesis Testing
9 Sep 2015
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Just as we did before, we will base both our confidence interval and our hypothesis test on a sampling distribution model (clt). Law of large numbers: as the number of randomly drawn observations (n) in a sample increases, The mean of the samples mean gets closer and closer to the population mean (quantitative variable). The sampling distribution of any mean becomes more nearly normal as the sample size grows, regardless of the shape of the parent population. The central limit theorem tells us (chapter 18) that the sampling distribution model for means is: normal, with mean and, standard deviation. Estimate the population parameter with the sample statistic s (sample standard deviation). The sampling model is no longer the normal model. If the parent population from which we are sampling is normally distributed then the sampling model is student"s t model with (n-1) degrees of freedom.
