Chapter 6 Designing Experiments

Section 6.2 Experiments in the Real World

Equal treatment for all All subjects are treated alike except for the treatments that the experiment is designed to compare Any other unequal treatment can cause bias But, treating subjects exactly alike is hard to do...

Placebo effect is real and strong In a study meant to help bald men keep their hair 42% of balding men maintained or increased the amount of hair on their heads when they took a placebo 86% of men getting a new drug to fight baldness maintained or increased the amount of hair on their heads

So... it would be foolish to tell subjects if they are getting the placebo or the new drug! This would weaken the placebo effect and bias the experiment in favor of the other treatment. Likewise, if the doctor knows who got a placebo vs. treatment, he may treat them differently based on his expectations.

Double-blind experiments Neither the subjects nor the people who work with them know which treatment each subject is receiving Only the statisticians know!

Refusals, nonadherers, and dropouts Bias can result if those who refuse are systematically different from those who cooperate Minorities, expecially blacks, are more likely to refuse to participate in clinical trials Some remedies for lack of participation: complete and clear information about the experiment insurance coverage for experimental treatments participation of black researchers cooperation with doctors and organizations in black communities

Refusals, nonadherers, and dropouts Nonadherers are subjects who participate but don't follow the experimental treatment Dropouts occur in experiments that continue over an extended period of time when a subject begins the experiment but does not complete it All three create bias!

Can we generalize? A well-designed experiment tells us that changes in the explanatory variable cause changes in the response variable. At least that happened for the specific subjects in the specific environment of the study... Can we then say that in happens in general???

Step 1 Be sure the findings are statistically significant Diminish the lack of realism from the contrived environment of the study Step 2

Experimental design in the real world Completely randomized design All the experimental subjects are allocated at random among all the treatments Can have any number of explanatory variables

Interaction A combination effect when two or more explanatory variables interact differently because of the combination of both occuring at the same time i.e. medication and alcohol

Matched pairs Matching subjects in various ways can produce more precise results than simple randomization One common design that combines matching with randomization is the matched pairs design Compares just two treatments

Matched pairs Choose pairs of subjects that are as closely matched as possible Randomly assign the two treatment to the subjects Matched pairs are an example of block designs

Block Design A block is a group of experimental subjects that are known before the experiment to be similar in some way that is expected to affect the response to the treatments. In a block design, the random assignment of subjects to treatments is carried out separately within each block. A block is a form of control Similar to stratified samples

Figure 6.5 A block design to compare the effectiveness of 3 TV advertisements. Female and male subjects form 2 blocks.

Randomization Control Enough subjects Continue to be keys to convincing experiments!