Stats Final Review.docx

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Pennsylvania State University
Maria Cipollone

CoreyYoung Mrs. Maria Cipollone Statistics for the Social Sciences 23 April 2013 Final Review • Normal Distribution i. Central Limit Theorem: if the sample is from a normally distributed population, the sampling distribution of any statistic from the sample will be normally distributed. *n=100 ii. Standard Error: the standard deviation of the sampling distribution iii.NormalityAssumption: the assumption that the population being studied is normally distributed along variable x. iv. Law of Large Numbers: if the size of the sample is sufficiently large, n=30, the Central Limit Theorem will apply even if the population is not normally distributed. • Significance Level i. Critical Value depends on the significant level. *p=0.05 or 5% ii. Type I Error: reporting significance, when there actually wasn’t. *Also known as: α-error iii.Type II Error: reporting no significance, when there actually was. *Also known as: β-error iv. Statistical Power: probability of making Type II Error. (β) v. Inflated Alpha (α): used to prevent committing Type I Error inAnalysis of Variance. (ANOVA) • One Sample Z-test i. Population parameters ii.  & µ • One Sample T-test i. Population Standard Deviation is not known. ii. n and x-bar • Two Sample T-test i. Independent: comparing two independent samples that do not require the same criteria. Ex. comparing sample to population *No Relation ii. Dependent: pre-existing relationship between the two samples. Ex. repeated measures *Sample to Sample iii. Pooled Variance: used on independent two sample t-test variance, when assuming the variances are homogeneous or equal, to estimate the population variance. iv. F-test: test for homogeneity of variances. • Statistical Significance i. Power: the likelihood of committing a Type II Error or β-error. ii. Cohen’s Coefficient of Determination- to determine statistical power (1-
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