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Final

# Stats Final Review.docx

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Pennsylvania State University

Psychology

PSYCH 200

Maria Cipollone

Spring

Description

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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