2. Producing Data Designing Experiments

3. 3 Basic Principals of Experimental Design Control Control the effects of lurking variables on the response, most simply by comparing 2 or more treatments Randomize Use impersonal chance to assign experimental units to treatments Replicate Repeat each treatment on many units to reduce chance variation in results CRR

4. Control The main idea in this part of the design is to eliminate or minimize the effect of lurking variables Our goal is to show that the response was caused by our treatment and not “confounded” by other variables Comparison is an aspect of control Compare two “groups” by giving one the treatment and one a placebo Looking for the “placebo effect” - where subject responds similarly despite receiving the placebo Control Group – group that receives the placebo treatment When you have a control group, both groups get the lurking variables, so you can focus on the effectiveness of your treatment!!!

5. Randomization This is the rule used to assign the experimental units the treatment Chance should be used to divide experimental units into groups (SRS – random #’s) This ensures the similarity of the groups receiving the different treatments The groups must be similar to further eliminate lurking variables and their influences If groups are assigned by the experimenter either by choice or through matching, bias is introduced Consider our caffeine study: Problems among the groups could occur if there are individuals that have a higher tolerance for caffeine, or are of different age or gender or race, etc. By randomizing the assignment of individuals to the groups, we look to “even” out the differences by creating similar groups which will reduce the effects of these variables. To “help out” the effectiveness of random assignment we should: 1)Use enough experimental units 2)Make sure Groups are of equal size 3)Stratify if necessary

6. Drawing Experimental Diagrams A food company wants to asses the nutritional quality of a new “instant breakfast”. They are going to examine a rat’s weight gain over a 28 day period by giving a control group of rats a standard diet. Random Assignment Group 1 15 Rats Group 2 15 Rats Treatment 1 New Diet Treatment 2 Standard Diet Compare Weight Gain

7. What’s the Goal of the Big 3? Control Control the effects of lurking variables on the response, most simply by comparing 2 or more treatments Randomize Use impersonal chance to assign experimental units to treatments Replicate Repeat each treatment on many units to reduce chance variation in results To produce an experiment with a result that is: STATISTICALLY SIGNIFICANT What’s Statistically Significant? An observed effect that is so large that it would rarely occur by CHANCE!! This means that we have good evidence to support the desired effect “There is not statistically significant evidence that caffeine causes a freak to go crasy!!! Thanks to Mummy!

8. Matched Pairs Design Matched Pairs Match subjects in a comparative experiment to ensure results are truly comparative Control vs. Experimental to 2 similar subjects By matching a subject with another of similar characteristics, I limit the effect of lurking variables (differences in subjects) That gives me a better chance of showing a CAUSAL relationship!!!

9. Making Inferences about the Population The Better the Experiment… The More Valid a Generalization can Be Generalizations about Causation must be examined on a case by case basis Each experiment should exhibit some element of the big 3: CRRCRR ontrol andomization eplication Select your Design based on your experiment… Just be sure to include all three elements!!