PSYCH 312 1st Edition Lecture 8 Outline of Last Lecture I. frequency distributionsa. table b. graphII. measures of central tendency a. mean b. median c. moded. shape of data seti. normal ii. skewed1. negatively2. positivelyIII. graphing dataa. bar graphb. line graph c. examplesOutline of Current Lecture I. evaluation of research hypothesis These notes represent a detailed interpretation of the professor’s lecture. GradeBuddy is best used as a supplement to your own notes, not as a substitute.II. variance inferential statsIII. types of variationa. chanceb. IV (systematic)c. Confound (systematic)Current Lecture -Example: warm/coldoPopulation-Sample (random selection)-Random assignment -Cold condition-Warm condition-Example: warm/cold oPossible hypotheses-Null:-Expectations of lecturer (as defined by the description) will have no affect on participants first impression of the lectureroCold condition-Impression rating=oWarm condition-Impression rating-Variance oDispersion/range of scoresoNot just low & high scores but how total number of scores sit in relation to the meanoSum of squares/N (N=total number scores)-Transform variance into same unit of measurement as original scores by taking square root--->Standard deviation-Types of variationoChance of error variance -Effect of extraneous variables on DV-EX: mood of participants, time of day, ect. -Affects DV in a nonsystematic manner-Creates differences in individual performance -AKA: within-group variance -NOTE: random assignment should help make within group variance similar across the conditions (but doesn’t eliminate these differences)oSystematic variance due to IV-Differences in DV observed in different conditions-AKA "between group variance"-How much dispersion of scores differs from one conditions to the next-If IV has no effect on DV, distribution of scores in different conditions will overlap-If IV does have an effect on DV, scores of DV will be different across conditionsoThe critical test is whether between group variance is larger than within group variance-EX: ANOVA-->F ratio-=btw-grp VAR/W/I-grp VAR-=treatment VAR + Change VAR/chance VARoIf IV has no effect on DV-Btw-grp Var/ w/i-grp var =1-Treatment=0 + chance Var/chance var=1oIf IV has effect on DV-Btw-grp Var/ w/i-grp var >1-Treatment=0 + chance Var/chance var>1oCould have systematic variation due to confound(s)-Some variable other than IV systematically affects DVoIf confound had effect on DV-Btw-grp VAR gets larger-F ratio gets larger (wrong reason)-Could lead to type 1 error -Reject null when its true -Conclude that there is treatment effect when there