Transcript Say “no”
Stats 95 • Experimental Design – Experimental Design & Lady Tasting Tea – Type I and Type II Errors – Null Hypothesis an Research Hypothesis Lady Tasting Tea • How would you design the experiment? • What task would you give her? • What would be the Independent Variable? Dependent variable? Control condition • How many could she guess right by chance? • What if she can taste the difference, but she makes mistakes? • Do you know for certain she can? Do you know for certain she cannot? Hypotheses • H0 Null Hypothesis: there is nothing going on, Straw Man, the probability of guessing Tea in Milk is equal to guessing Milk in Tea • H1 Research Hypothesis: something is going on, probability of correct identification is not equal to guessing between Milk in Tea and Tea in Milk. Hits (response “yes” on signal trial) Probability density Criterion N Say “no” S+N Say “yes” Internal response Correct rejects (response “no” on no-signal trial) Probability density Criterion N Say “no” S+N Say “yes” Internal response Misses (response “no” on signal trial) Probability density Criterion N Say “no” S+N Say “yes” Internal response False Alarms (response “yes” on no-signal trial) Probability density Criterion N Say “no” S+N Say “yes” Internal response “What Cold Possibly Go Worng?”: Type I and Type II Errors Reality Perception YES (Signal + Noise) YES Hit (Signal + Noise) NO (Noise) Miss (Type II) False Negative NO (Noise) False Alarm (Type I) False Positive Correct Rejection “What Cold Possibly Go Worng?”: Type I and Type II Errors REALITY of PREGNANCY YES NO HIT (Pregnant & “+” on test) FALSE ALARM (Type I) Also called False Positive (Not Pregnant & “+”) TEST RESULTS “PREGNANT” (Reject Null) “NOT PREGNANT” (Fail to reject the Null) Miss (Type II) Also called False Negative (Pregnant & “-”) Correct Rejection (Not Pregnant & “-”) The End Statistics in Correlations & Experiments • Correlations measure Relationship – Strength and direction of relatioship • Experiments measure the Differences – Statistical significance of the difference Correlation: Measuring Relationship • Sir Francis Galton (Uncle to Darwin – Development of behavioral statistics – Father of Eugenics – Science of fingerprints as unique – Retrospective IQ of 200 – Drove himself mad just to prove you could do it – Invented the pocket 2.3 The Science of Explanation • Measuring correlation – more-more/less-less – more-less/less-more • Correlation coefficient – – – – measure of direction & strength r = 1 r = -1 r = 0 13 Correlation • What does correlation coefficient mean? 14 2.3 The Science of Explanation • Experiment—2 critical features • (1) Manipulation – independent variable – dependent variable—measured – Control Group Condition (or Variable) – Experimental Group Condition (or Variable) • (2) Randomization - controls for a 3rd variable (you know exists but are not interested in) – versus self-selection 17 Dependent Variables Without Demand Charcteristics • DVs that aren’t subject to biased responses • Examples: – Is a painting in a museum popular? • There will be increased wear on the carpet near it. – Did a dental flossing lecture work? • Students will have cleaner teeth the next day. – Did a safer sex intervention for commercial sex workers work? • There will be more condoms discarded in the park they work in. Variation in IV Causes Variation in DV 1. Cause → Effect: whenever IV occurs, outcome DV should result. Safe sex intervention Condoms in Park 2. Cause absent → Effect absent No SS intervention no condoms 3. Cause variation → Effect variation More or better interventions more condoms in park Experimental & Control Groups • Experimental Condition: Cause is valid – E.g., drug, alcohol • Control Condition: cause • Essence of experiment is to is invalid control conditions beforehand – Placebo, juice The Science of Observation • Validity—able to draw accurate inferences – construct validity: e.g., describing what intelligence is and is not, “construct” refers to the “theory” – predictive validity: over time you find X predicts Y • Reliability—same result each time? - Test/Re-Test - Parallel - Inter-Item 21 Statistical Significance • A finding is statistically significant if the data differ from what we would expect from chance alone, if there were, in fact, no actual difference. • They may not be significant in the sense of big, important differences, but they occurred with a probability below the critical cutoff value, usually a z-score or p < .05 • Reject or Fail to Reject the NULL Hypothesis Graphing Frequency Discrete: Histogram Continuous: Frequency Polygon Stem-and-Leaf: Exam 1 & 3 Selection of ranges & bins like Histogram, but usually simpler. These plots represent the scores on an exam given to two different sections for the same course.