PSYB07H3 Study Guide - Final Guide: Central Limit Theorem, Type I And Type Ii Errors, Sampling Error

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12 Jun 2013
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Normality is a common assumption of inferential statistics. Models can be used to calculated likelihood of outcomes. It has a mean, standard deviation, is unimodal, symmetric and mesokurtic. On the table, the value of one specific outcome is 0 because conceptually, there are infinite possibilities, so one value would be so small. Linear transformations of mean state that whatever you do to original, you do to the other. Linear transformations of variance state that if you add or subtract, you don"t change variance, but if you multiply or divide, you multiply or divide by the square. The 68-95-99 rule says that in normal distributions, the z score will likewise have 1-2-3 sd of mean. Sample statistics estimate population parameters with varying degrees of success. Sampling error is the difference between a statistic and its parameter. Standard error is sampling error that tells us how much to expect sample means to vary (put in graphs).