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· Says that 95% of the sample means will fall within a given confidence int.—the μ will fall w/in this as well
· Both hypoth and conf int based on alpha level—foundation material is similar
· Hypoth testing based on null hypoth, conf int based on actual outsomes (range)
· Suggested as an alt to hypoth testing
· Follow up to 2 tailed· Interval estimate is confidence int
· In std dev units
· Shows how much a treatment actually effected a condition
· Measure of magnitude· Significance only tells if something had an effect or not, effect size tells you how much of an effect it actually had
· Easier to have significance but small effect size with large n· Ability to detect a change in the data if such a change actually occurred
· Power = 1 – β
· Can see if a treatment will be effective before testing is actually done
· Power increases as type 2 error decreases· Sample size increase power increase—width shrinks
· Direction -> non directional—alpha decreases, beta increases, power decreases
· Discrepancy to be detected- position of alt curve changes—difficult small differences
· Alpha changes- decreases, beta increases, power decreases—goal line changes
· Variability – position shifts, variability increases, power decreases26. What are some criticisms of, or concerns regarding, traditional hypothesis testing?
· Arbitrary significance—not based on actual consequences of type 1 error
· Dichotomous logic--black and white!
· Overemphasis on significance
· Inadequate attention to other factors that influence significance- i.e. sample size, variance(poor control)· Confidence interval—alpha level still arbitrary
· Alpha level based on risk –what constitutes risk?
· Report actual probability and reader can determine significance
· Confidence int:---The actual data found, 95% of the sample means should fall within this interval—still based on alpha (CLT)—not dichotomous like hypoth testing
· Hypoth. Testing focuses on the null (what didn’t happen)
o -black and white-no gray.
· Effect size---how many std dev did the treatment chance the control
· Proportion of variance tells how much of the total variance can be accounted for by the study done
· Correlation equation subtracts out consistent indiv differenced right away
· S_{d}/√n uses the individual differences variation right away
· It is multiplies by the error that gets subtracted out therefore the better the correlation the lower the error.
· The better groups are matched the better the correlation the lower the error
· Power is greater for dependent because there is less error
· Based on degrees of correlation- r increases power increases· PROS – better power, more correlation, less error, subtract out individual differences
· CONS – difficulties in matching, carryover effects, loss of extremes
· Don’t need to know variability or difference to be detectedàneed to know to know power, so good that we don’t have to know them
· Estimate ratio of cohens d—what effect size do you want?"StudyBlue is great for studying. I love the study guides, flashcards, and quizzes. So extremely helpful for all of my classes!"
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