1. SCI 105.020 Scientific Inquiry Evaluating Causal Hypotheses

2. Causation vs. Correlation -similarities Relationships between two variables Associations in sample statistics can be observed and analyzed in the same way  Direction  Strength  Margin of error (ME) Small Large Red Green Not E E x-group k-group A random experimentA jar of marbles

3. Causation vs. Correlation -differences Correlation is a symmetric relationship, whereas causation is not In a causal relationship  There is a clear temporal order between the cause and effect variables: you cannot cause something happen in the past  Causes produce their effects in the causal production 10/40 (or 25%) Large 45/60 (or 75%) Large Red Green 15/45 (or 33%) Red 45/55 (or 82%) Red Large Small Being Large is positively correlated to being Red, and vise versa. Use ashtray Lung cancer Smoking correlation causation

4. Differences In Hypothesis Evaluation Evaluating correlation  The real-world population Population sampled vs. population of interest  The sample data Sample sizes and observed frequencies  The statistical model  Random sampling Representativeness and possible biases  Evaluating the hypothesis Strength of correlation  Summary Evaluating causation  The real-world population & causal hypothesis Identify C & E variables and state the hypothesis  The sample data  Design of the experiment Random/prospective/ retrospective  Random sampling  Evaluating the hypothesis Effectiveness of the causal factor  Summary

5. The Ideal Case Ideally, we’d like to divide the real-world population into two disjoin group:  Subjects exposed to the cause variable  Subject not exposed to the cause variable exposed not exposed x-group k-group Not E E x-group k-group

6. Random Experiment In reality, one can only mimic the ideal condition by random sampling exposed not exposed x-group k-group population of interest population sampled Random sample Not E E x-group k-group Random sampling

7. Principles of Experimental Design The fundamental principle is control  The key is to control for possible effects of extraneous variables Principles of control include  Comparison: observe any differences in effect variable  Randomization: impacts of extraneous variables can be balanced out These can then be attributed directly to the cause variable  Blindness: further improve the effectiveness of the experiment by resolving the placebo effect

8. Prospective Studies Not controlled experiments, inherently not as strong Widely used in various medical contexts where a real controlled experiment is not applicable Efforts can still be made to minimize the impacts from other variables (O)  Select only subjects not exposed to O  Select only subjects exposed to O and make sure there is no interaction between o and the suspected cause  Matching the subjects according to their values in O

9. Prospective Studies -an illustration x-group k-group population of sampled Random sampling E E C Not C Starting from a real population in which its instances have selected their groups already Random sampling is still achievable in selecting subjects from the divided population

10. Retrospective Studies Retrospective studies mean to survey the past  Just opposite to prospective studies, which look into the future Starting from a x-group with subjects that are known to have the effect and a k-group which is free from the effect  Ideally the subjects in the k-group will match up with the x-group subjects in all other aspects Efforts are needed to verify it as far as possible  The result of the study was presented as percentages of subjects who had the cause, not percentages of those who developed the effect

11. Retrospective Studies vs. Survey Sampling Both employ techniques such as interviewing Both can come up with various frequency values A survey can also select subjects randomly Why should one bother to do a retrospective study?  The difference is numbers! CasesControls All women 62%52% Women w/ children 61%52% Women w/o children 65%51% Women using pills before 22 yrs 68%69%