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# Psych 7 Final Study Guide 7

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This 8 page Study Guide was uploaded by Debbie Chen on Friday December 12, 2014. The Study Guide belongs to 7 at University of California Santa Barbara taught by PROTZKO in Fall2014. Since its upload, it has received 99 views.

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Date Created: 12/12/14

PSYCHOLOGY 7 FINAL STUDY GUIDE JOHN FOLEY Times amp Dates 0 Tuesday 845 Foey s OH Review Psych 3834 0 Wednesday FINAL 1200 Test 0 Cumulative on what we missed during the midterm amp final 0 Parscore This review guide contains 0 Week 10 Lecture Notes 0 Outline of important concepts from post midterm2 Week 10 Bayesians Office Hours Psych 3834 Tuesday 845 1045 AM John 0 PDH0 0 Null Hypothesis testing 0 How likely would the outcome have happened randomly 0 Cancer problem 0 If you want to know you have a specific type of cancer 0 1 rate 0 You take a test that is 90amp accurate 39 If you have cancer the rest has a 90 chance of coming out positive 7 FP You get a positive result What is the likelihood you have the cancer 125 Why OOOO 0 Bayesian tells you the probability of the hypothesis given the data 0 Baserate is the probability of the hypothesis 0 And then you get a test result with Bayesians Breast Cancer 0 Average cancer rate 00628 0 Probability of Sam having breast cancer 0 00628 0 Positive mammogram 0 False positive How likely are you to have this data if your hypothesis is wrong 6 0 True positive How likely are you to have this data if your hypothesis is right 78 0 Mammogram 0 False positive 60 0 True positive 78 0 PH 0000629 0 PDH 078 0 PDH 06 0 PD 0600113 0 PHD 78 x 0000629 600113 00008 0 PHD PDH x PH PD 0 Probability of cancer Probability of true hypothesis x general probability of true false Sam amp Baserates 0 Sam 01243 0 PHD 0001615 0 Samuel 00013 0 PHD 000017 0 Shows us that base rates really affects things 0 Things that are really rare are really rare no matter what your results for a test are 0 BASE RATES MATTER A LOT Bayesians 0 Gives you the probability of your hypothesis given the data 0 Take Home Message 0 Base rates matter 0 You probably don t have that super rare disease 0 These are degrees of belief 0 What does this mean NHT only gives you PDHo Bayesianism lets you start talking about your hypothesis PHD Even though 80 of shark attacks happen in less than 3 feet of water it doesn39t matter bc most people are in less than 3 feet of water 0 Not impressive 0 Bayesianism in research 0 They don t use the equation 0 What they use is Bayes Factor comparing one hypothesis to another hypothesis given the same data 0 Prior before data what is the probability of your hypothesis being true 39 How I set my priors matter 0 Bayes Factor 39 One over the other 39 H2 could be the null or a competing hypothesis 39 Bayes Factor 30 40 counts as a good enough evidence 39 Is that any less arbitrary than plt005 How do we must treat Major Depressive Disorder 0 We know SSRI help 0 What about exercise 0 RCT 101 patients with mild depression 0 Placebodrugexercise group 0 Exercise amp Serotonin both helped depression 0 But no difference between the groups 0 P 607 tells us we can39t interpret it doesn39t tell us how much 0 But Bayesian statistics you can how much better is exercise than SSRI 0 In general 0 Bayesianism says a good hypothesis is something that takes super extraordinary data and makes it ordinary Pay attention to base rates Bayesianism comes in degrees 01 no such thing as believe and don t believe Belief cannot be 0 or 1 PH 0 then PHD 0 PH 1 then PHD 1 OOOOO 0 Makes the equation unchangeable 0 Problems 0 Declaring PH 39 Uninformed priors increases Bayes Factor 0 Subjective 0 Endless ways to be wrong Lecture 2 Research Ethics The Tuskegee Syphilis Study 0 Progression of untreated syphilis 0 Placebo treatment 0 Didn t give penicillin because they wanted to know progression Forced Sterilization of the mentally unfit 0 Started in America 0 Upheld by the supreme court 0 Technically still legal today Nazi tested for typhus fever 0 Either get a vaccine or placebo then injected with infected blood 0 Nuremberg Code voluntary informed consent valid research that could provide fruitful results 0 Must volunteer and give informed consent 0 Deceptionomit can be okay 39 le bystander effect 39 But must have good reason Willowbrook Hepatitis Study 0 What is the progression for untreated hepatitis 0 Used mentally defective children 0 Tested effect of gamma globulin in treatment 0 Closed doors to new inmates 0 Can accept if new inmates if part of study 0 Cannot force people to be part of your study 0 Prisoners 0 Cognitively Impaired 0 Minors 0 7 child and adult 0 7 adult Tea Room Study 0 Bathrooms for homosexual sex 0 Stood outside and recorded license plates 0 Went to their home and disguised themselves amp went through social health survey 0 Wrote book families of men found out 0 Must disclose privacy and confidentiality Milgrim Study 0 Must have debriefing 0 If you deceive you must debrief Data Faking 0 Andrew Wakefield 0 MMR Autism 0 Fake Data 0 Trying to bring lawsuits against drug companies 0 Jan Hendrik found to fake data 0 No one could replicate it 0 Too perfect data 0 Diederik Stapel 0 Fake data 0 The people he worked with came forward 0 Must rely on scientific integrity 0 Inability to replicate Important Concepts Longitudinal Design 0 Cohort Cohort effects same people 0 No manipulation 0 Advantage fewer people sensitivity individiual differences 0 Disadvantage order effects dropout 0 Predicting ONLY ABOUT PREDICTING NOT CASUALITY 0 Controlling for making the slope for pretest equal changes based on observed scores 0 Multiple regression used when we want to predict the value of a variable based on others 0 Example predicting what we will look like in the future 0 High causality with no actual causality are called spuirious correlations Case studies 0 Phineas Gage 0 Genie 0 Frere Jacques 0 Split brain patients 0 GeneralizabilityLaw of large numbers 0 Solutions replicate more numbers ignore individual differences mechanism 0 Single Case Research Design 0 Multiple Baseline 0 Stable Baseline 0 NaturalisticSystematic Observations Statistical Significance 0 Hypothesis 0 ProbabilityDataNul Hypothesis 0 P value tells you the probability that even if the null hypothesis is true the chance in which you got the result randomly 0 Rejecting null hypothesis ONLY TELLS YOU THERE IS AN EFFECT 0 The higher the P the lower the T 0 P effect sizeN 0 Cohen39s D tells you the overlap between two groups in SD units 0 Type I amp Type II Error 0 P hacking is when you change the statistics to make it significant like using a lot of DV 0 Needs to be replicable 0 Power is the ability to correct reject 0 Type III is when it39s in the wrong direction 0 Parametric aassumptions about populationNonparametric nomnal 0 Interactions are the differences between main effects 0 IV Main effect 0 Levels within main effect 0 Confound alternate explanation 0 Moderator conditional cause 0 NEVER DICHOTOMIZE A CONTINUOUS VARIABLE Bayesians 0 Bayesian probablity of hypothesis given the data 0 Baserate probability of hypothesis before the data 0 True positive how likely the data is if your hypothesis is right 0 False positive how likely you are to have this data if your hypothesis is right 0 Base rates matter a lot 0 Degrees of belief in Bayesianism 0 Bayes Factor compares two hypotheses given the same data Ethics 0 Tuskegee Syphilis Study 0 Willowbrook Hepatitis Study 0 Tea Room Study 0 Milgrim Study

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