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Unformatted text preview: Significance Testing Statistical testing of the mean Binomial Distribution Mathematicians have figured formulas to estimate long run relative frequencies for simple events, like how many heads will appear for a given number of coin tosses. The binomial is one such. 12 10 8 6 4 22 PROB .3 .2 .1 0.0.1 Number of ‘heads’ in 10 flips of a coin. Recall dice Normal Distribution We have already figured percentages of the normal. Percentages of the normal correspond to probabilities of finding individual cases in the distribution. The sampling distribution of the mean is normal if N is large. 3 2 1123 Scores in standard deviations from mu 0.4 0.3 0.2 0.1 0.0 Probability (Relative Frequency) Standard Normal Curve Standard Normal Curve Standard Normal Curve Standard Normal Curve 50 Percent 34.13 % 13.59% 2.15% Middle 95 percent from going up and down 1.96 SDs from the mean. Significance Testing 1 Significance testing is a ‘what if’ game. We make an assumption, and ask what will happen if the assumption is true. Assumption is null hypothesis . Significance testing is based on probabilities that come from the ‘what if’ scenario (from the null hypothesis). What if the true mean height of students at USF is 66 inches and SD is 5 inches? What if we draw people from USF 100 at a time and plot the means?...
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 Spring '10
 Brannick
 Statistics, Normal Distribution, Standard Deviation, Statistical hypothesis testing, significance testing

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