1. Chapter 6 STA 200 Summer I 2011

2. Equal Treatment of All Subjects The underlying assumption of randomized comparative experiments is that all subjects are handled equally in all respects except for the treatments being compared. If the subjects aren’t handled equally in other respects, there might be bias.

3. Knowledge of Placebos If a patient knows that they are receiving a placebo, we won’t be able to gauge the extent of the placebo effect. If a doctor knows that a patient is receiving a placebo, the doctor may treat the patient differently.

4. Double-Blind Experiments A double-blind experiment is one in which: The standard for experiments is a “randomized, double-blind, placebo- controlled trial.”

5. Example A researcher told 13 people who were sensitive to poison ivy that the stuff being rubbed on one of their arms was poison ivy. It was really a placebo, but all 13 broke out in a rash. The stuff rubbed on their opposite arms was really poison ivy, but they were told it was a placebo. 2 of the 13 people broke out in a rash.

6. Problems with Experiments Refusals – individuals who refuse to participate – problem for experiments dealing with major diseases Nonadherers – don’t follow their assigned treatment – may not take treatment as prescribed, or take additional treatments on their own Dropouts – begin the experiment, but don’t complete it – bias occurs if dropouts happen due to a reaction to one of the treatments

7. Generalizing Experimental Results In order to generalize results from a group of experimental subjects to the population of interest: – The results must be statistically significant. – The environment of the experiment should be realistic. – We can only generalize for the population considered in the study.

8. Completely Randomized Design The basic kind of experimental design is a completely randomized design, where the subjects are randomly allocated among all the treatments. SubjectsGroup 1 Treatment A Group 2 Treatment B Group 3 Treatment C

9. More Elaborate Experimental Designs Multiple explanatory variables: – You can have them if you want. Matched Pairs Design: – useful for comparing two treatments Block Design: – useful for comparing subgroups

10. Matched Pairs Design Two treatments are compared There are two possible designs:

11. Matched Pairs Design Example An experiment to determine if hypnosis affects learning: Each subject learns two lists of 16 word-number pairs (one awake, one hypnotized). The lists are similar in difficulty, and the order is randomized. After listening several times, the subject repeats the list. The response variable is the number of errors.

12. Another Matched Pairs Example In order to determine if using a new instructional software in the classroom improves test scores in middle schools, we first find two similar schools. We then randomly assign the software to one of the schools. The other school continues with their current routine.

13. Block Design like stratified random sampling for experiments A block of subjects is a group known to be similar in some way that is expected to affect the response to the treatments. First, the group of subjects is broken up into blocks. Treatments are then assigned randomly within each block.

14. Block Design Example An experiment comparing a low-fat and low- carbohydrate diet on weight loss: Researchers are concerned that the effect of diet may depend on gender, so gender is treated as a blocking variable. There are 122 severely obese subjects: 52 men and 70 women.

15. Block Design Example (cont.) 122 subjects Block 1 52 men Group 1 26 men Treatment A low-fat diet Group 2 26 men Treatment B low-carb diet Block 2 70 women Group 3 35 women Treatment A low-fat diet Group 4 35 women Treatment B low-carb diet