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Final Review.docx

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Dwayne Pare

Normal Distribution Normality is a common assumption of inferential Power (hit): probability of identifying an outcome statistics. Models can be used to calculated likelihood of when there is one (when null is false, if null is true, outcomes. It has a mean, standard deviation, is there is no power), increase by: unimodal, symmetric and mesokurtic.  Increase effect size  Reduce Variability On the table, the value of one specific outcome is 0  Increase n (CLT) because conceptually, there are infinite possibilities, so  Use 2-tailed to have power on both sides one value would be so small.  Increase alpha (but not preferable as that increases Type I error probability) Linear transformations of mean state that whatever you do to original, you do to the other. Linear Central Limit Theorem: for a random sample of transformations of variance state that if you add or observations from any distribution with a finite mean subtract, you don’t change variance, but if you multiply and a finite variance, the mean of the observations will or divide, you multiply or divide by the square. follow a normal distribution. This is the main reason of widespread use of statistics based on the normal The “68-95-99” Rule says that in normal distributions, distribution. the z score will likewise have “1-2-3” SD of mean. T-Tests: used when population SD is not given Hypothesis Testing: Z Scores  One sample: when population is being Sample statistics estimate population parameters with compared against (given population mean) varying degrees of success. Sampling error is the  Related: When it’s the same person doing difference between a statistic and its parameter. different tests (have more power than ind. Standard Error is sampling error that tells u
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