Cautions about Experiments (and a little review)

1. Cautions about Experiments(and a little review) AP Statistics Presentation 3.9

2. Cautions about Experiments • When dealing with experiments bias and lurking variables, once again, may show up. • To minimize their effect • Use blocking to account for the variables you know about • Use randomization to account for the variables you do not know about

3. Cautions about Experiments • Hidden Bias • It is imperative that all treatments are administered equally and all variables are measured equally • This can be rather difficult in practice. • Imagine testing pain medication. A patient will typically rate the pain on a scale of 1 to 10. The scale should be (though it cannot) consistent patient to patient. It should furthermore be explained in an identical manner by the experimenter.

4. Cautions about Experiments • Blind vs. Double Blind • It is important (especially in the medication examples) that the patients not know (are blind to) whether they are receiving the medication or the placebo (they probably don’t even know a placebo exists). • Especially when the measure of the variable is subjective, it is important that those people who interact and gather the data also do NOT know whether the patient received the treatment or the placebo. • This is referred to as Double-Blind. • In a double blind experiment, neither the subjects nor the people working directly with the subjects knows which treatment the subject had.

5. Lurking Variables • Recall the lurking variables from Unit 2 • Confounding variables • Variables that affect the response variable • For example, SAT scores have decreased over time (historically) • A lurking variable is the number of students taking the exam. As a greater proportion of the population takes the exam, the scores naturally drop (less able students are more frequently taking the exam). • Common Response variables • Variables that affect the explanatory and response variables in tandem • For example, drinking bottled water increases the healthiness of children. • A lurking variable is the wealth of the parents. Parents that can afford bottled water tend to also be able to afford good quality health care.

6. Treatment Group #1 (Block A) Treatment Group #2 (Block B) Measure Variable (s) Randomly Allocate Treatment Group #3 (Block C) Control Group Recall Experimental Design Layout Compare Results Experimental Units (Subjects)

7. Practice #1 • Design an experiment for the following scenario (draw the diagram and supplement your work with a few sentences incorporating appropriate vocabulary): • You are to design an experiment to determine whether a calcium supplement in the diet will reduce the blood pressure of middle-aged men. Similar studies suggest that there may be differences in the effectiveness between blacks and whites. You have 100 subjects available for the study. • An example solution is on the next slide.

8. 25 White men Calcium Supplement 25 Black men Calcium Supplement Measure Blood Pressure Randomly Allocate 25 White men Placebo 25 Black men Placebo Practice #1 Solution Compare Results 100 subjects This is an example of how you might block an experiment on race. With this design, you would be able to determine the effects of the supplement on both races.

9. Practice #2 • Design an experiment for the following scenario (draw the diagram and supplement your work with a few sentences incorporating appropriate vocabulary): • You are to design an experiment to determine whether the right hand is generally stronger than left hand. You can crudely measure hand strength by squeezing a bathroom scale with your hand. The reading of the scale shows the amount of force applied by the hand. Design an experiment given that you have 10 subjects to work with. • An example solution is on the next slide.

10. Practice #2 Solution 5 subjects Test right hand 1st, then left hand. Compare Results 10 subjects Measure Difference in strength Randomly Allocate 5 subjects Test left hand 1st, then right hand. This is an example of how you might use a matched pairs design. Each subject acts as their own control and the differences in hand strength is the data that is analyzed.

11. Cautions about Experiments • This concludes this presentation.