Chapter 3.1

 Observational Study: involves passive data collection (observe, record or measure but don’t interfere)  Experiment: ~Involves active data production (actively intervene by imposing some treatment in order to see what happens) ~Randomly assigns subjects to these treatment levels ~Compares the responses of the subject groups across treatment levels. ~A well designed experiment can tell us that a certain treatment caused a certain response in our group of subjects (this is something a sample or observ. Study cannot do)

Subjects (participants): humans who are experimented on; individuals studied in an experiment Explanatory variable: variable we are testing, variable that we think explains changes in a response variable Response variable: variable that measures an outcome or result of a study, answers the question “How are we measuring?” Treatment: “what we do” to each subject (explanatory variable). If the experiment has several explanatory variables, a treatment is a combination of specific values of the variables. ~Subjects can have many different treatments Experiment Vocabulary

Students want to know if a Kaplan SAT review course helped their SAT scores more than students who didn’t take the course. Identify: 1. Subjects 2. Explanatory Variable 3. Response Variable 1. Students 2. SAT course 3. Change in scores Example

 Control  Making conditions as similar as possible for all treatment groups  We control all sources of variation we can think of  We use a control group to help us know that observed effects are due to the treatment itself, rather than some other factor  Randomize  Reduces bias  Use chance to assign subjects to treatment

 Replication  Repeat the experiment; data should be similar  We do this to have as much data as possible  Blindness ◦ Single Blind ◦ Double Blind  Blocking (talk more in 3.2)  Reduces bias  If we cannot control something than we need to block the experiment - We can block by age, gender, exercise habits, pre-existing conditions, etc….

 Lurking variable: ~Most common in observational studies ~Hidden variable(s) ~Could have an important effect on relationships among variables in the study but is not one of the explanatory variables studied.

 Confounding: ~Most common in experiments ~When their effects on a response variable cannot be distinguished from one another (could be either explanatory or lurking variable). ~Can occur due to poor design in an experiment ~Sometimes there is no way to avoid this.

 A soccer coach wanted to improve the team’s playing ability, so he had them run 2 miles a day. At the same time the players decided to take vitamins. In two weeks the team was playing noticeably better, but the coach and players did not know whether it was from the running or the vitamins. Here the vitamins (lurking variable) are confounded with the running (explanatory variable)  How could the coach make his experiment better?  Have 2 groups. One that was running 2 miles a day and one that was not. Then the lurking variable (vitamins) would cancel each other out b/c it would be happening to both groups.  What reduces confounding? Comparison. Lurking variables will cancel each other out if they work equally on both groups.

 Single blind:  Subjects do not know what treatment they are receiving BUT those who evaluate the results know  People involved can tend to adjust their behavior without knowing it (subjects, technicians, etc….)  Double blind:  Neither subjects nor the people who work with them and evaluate the results know which treatment each subject is receiving

 Statistically Significant ◦ If an observed effect is so large that it would rarely occur by chance ◦ If the experimenter decides that the results are bigger than chance, we will attribute the differences to the treatment.

 Placebo:  Dummy treatment (looks/tastes like the treatment)  Can be used to compare a new treatment with a treatment already on the market.  Placebo Effect:  a treatment often works if you believe it will  Control group:  Comparison group  Group who gets the non-treatment

1. Comparison – controls lurking variables 2. Randomness – reduces bias 3. Replication – reduces variability (repetition of an experiment) 4. Blindness – reduces bias

 1. Randomized Comparative Experimental Design: uses both comparison (2 or more treatments) and randomization of treatments to subjects

2. Matched Pairs Design: combines matching with randomization ◦ Chooses pairs of subjects that are closely matched as possible and each gets one treatment assigned by chance OR ◦ the “pair” is just one subject who gets both treatments one after the other (order is by chance)

 3. Block Design: If when we are conducting an experiment we suspect one group will respond differently than another group to an explanatory variable we block  separate subjects that share characteristics that might affect response to treatment.  Similar to Stratifying in samples.

 Sample surveys suffer from errors like non- response due to failure to contact some people. Experiments with human subjects suffer from similar problems.  Refusal - people who do not want to participate  Nonadherers - subjects who participate but don't follow the experimental treatment (take other treatments, add other medications, etc...)

 Dropouts - subjects who begin the experiment but do not complete it  If reasons for dropping out are unrelated to the experimental treatments, no harm is done other than reducing the number of subjects.  If subjects drop out because of their reaction to one of the treatments, bias can result.