· Says that 95% of the sample means will fall within a given confidence int.?the ? 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 ? ?
· 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 decreases
26. 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· Sd/?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?
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