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Inferential statistics
infers the characteristics of a population

null hypothesis
Hypothesis that concludes there is no relationship or difference in a measure

level of significance
probability of being wrong in rejecting the Ho
reported as p = x; typically level of significance is <.05

Type 1 Error
Reject Ho but it turns out there is no difference or relationship in the population

Type II Error
Fail to reject the Ho when there IS a difference/relationship in population

alpha level
level of significance set prior to data collection as a criterion for rejecting Ho

level of significance affected by 3 factors
 1. groups being compared  greater the difference, smaller the p value
 2. degree of sampling and measurement error (SD) lower the error, smaller the p value
 3. size of the sample (N)  large sample, p will have smaller value than in small sample

confidence intervals
provide a range of values in which the population or "real" trait value lies with specific probabilities

confidence intervals measured by:
 1. using sample data, calculate standard error of the mean Sx
 2. this value used to create intervals around sample mean that correspond to the probability of obtaining a population value in that interval
 ex  sample mean is 60; researcher has 95% confidence interval of 4872  95% chance that population or true mean is in that interval

Effect size
way of quantifying the degree of difference b/t two groups; also coefficient of determination
X1X2/SD = cohen's D

parametric tests
used when certain assumptions can be made about the data  normally distributed, equal variance, interval level measures

ttest (parametric)
 tests null hypothesis
 1. independentsamples ttest: different subjects in each group
 2. paired dependentsamples/correlated/matched : subjects in the groups are paired or matched in the same way

degrees of freedom (df)
 used to calculate the level of significance
 approximately equal to # of subjects in the study

ANOVA
 simple analysis of variance
 compares group means to determine the probability of being wrong in rejecting Ho (like ttest)
 independent variable has multiple levels

Simple/OneWay ANOVA
 single independent variable analyzed w/single dependent variable
 ex: study 3 types of students and means... students from SES h/m/l. 1x3 ANOVA
 F statistic calculated from variance of the groups

TwoWay Anova
 factorial analysis of variance
 2 or more i.v.s are analyzed together
 test for each i.v.
 ex: one i.v. has 2 levels, one has 3, 2x3ANOVA

ANCOVA
 analysis of covariance
 adjusts for pretest differences b/t groups
 pretest is the covariate
 ex: 1 grp has mean of 15 and other has mean of 18 on a pretest; ANCOVA used to adjust posttest scores statistically to compensate for 3 pt difference

Multivariate Statistics
 two or more dependent variables are analyzed together
 MANCOVA
 Hotelling's T

Chi Square
 x^{2}, c^{2}^{}
 used when researchers are interested in # of responses or cases in different categories
 results reported in a contingency table
 ex: relationship b/t gender and book choice
 m/f, 4 book types to choose from, 2x4 table

