Ch.1 psych notes

Ch.1 psych notes - NOTES (6/7/10) Cause Effect This is when...

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NOTES (6/7/10) Cause Effect This is when you isolate something to make something else ex: X-> Y Correlation doesn’t imply causation (but Mahan says that this is not true…it can cause causation) ---so two things happen at the same time but one doesn’t necessarily affect the other --Confound variable: X is controlling Y through Z so: X->Z->Y Statistics in general doesn’t tell you anything about Cause-Effect --Think about drug/medicine example given in class. --Independent variable: one that changes; ex: # mg --Dependent variable: the measured value that depends on the independent variable --hypothesis: more drug=smarter --statistically significant difference? --The mean/avg between the two groups should be the same if no statistical difference, but if there is a difference it is random/accidental --Difference: either RANDOM (no systematic difference) or SYSTEMATIC (yes “ ”) --if we do get a statistical difference, it is systematic Random Sampling : to randomly select a group of people so that once you study them, those results are representative of the entire population because the people were chosen randomly. causality has NOTHING to do with statistics SO cause effect is a logical concern; depends on HOW you DESIGN the experiment. H 0 = Null hypothesis (“bad news”—says that no matter how well you design the experiment any relationships that you establish are just coincidence/random) H 1 = Alternate hypothesis (never tested) –this is YOUR hypothesis H 2 and H 3 are other people’s/somebody else’s hypothesis (these try to refute your hypothesis) Ex: Smoking causes cancer --never been proven/only been tested on animals --based on correlation implies causation paradigm—framework to ask questions in
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This note was uploaded on 01/20/2012 for the course PSYC 3980 taught by Professor Claffey during the Summer '09 term at UGA.

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Ch.1 psych notes - NOTES (6/7/10) Cause Effect This is when...

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