Chapter 9

infers the characteristics of a population

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

probability of being wrong in rejecting the Ho

reported as p = x; typically level of significance is <.05

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

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

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

• 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

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

• 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 48-72 - 95% chance that population or true mean is in that interval

way of quantifying the degree of difference b/t two groups; also coefficient of determination

X1-X2/SD = cohen's D

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

• tests null hypothesis
• 1. independent-samples t-test: different subjects in each group
• 2. paired dependent-samples/correlated/matched : subjects in the groups are paired or matched in the same way

• used to calculate the level of significance
• approximately equal to # of subjects in the study

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

• 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

• 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

• 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

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

• x², c²
• 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