Experiments

Vocabulary  Response Variable – One that measures an outcome or result of study (dependent variable, y)  Explanatory Variable – One that explains or causes a change in the response variable. (independent variable, x)  Subjects – individuals studied in an experiment.  Treatment – a specific experimental condition applied to the subjects.

Confounding  Lurking variable – one that has an important effect on the relationship among variables but is not one of the explanatory variables studied  Two variables are confounded when their effects on a response variable cannot be distinguished.  Example the Nova U study of web courses

Placebo Effect  Placebo is a treatment with not active ingredients so should have no medical effect.  Placebos have often had a positive effect on people’s condition.  Placebo effect is the response to the dummy treatment.  Example – Gastric Freezing, placebo and freezing are confounded  Hawthorne Effect is similar.

Randomized Comparative Experiment  Randomly assign subjects to groups you want to compare.  Apply different treatments to each group  Do some statistics to compare results.  Hope for statistically significant results.  Need to have “enough” subjects.

Cycle cell anemia example

Electrical Metering

Statistical Significance  Even if the results of the comparisons are different, they may not be different enough to make a judgment.  We would like to know that it is very unlikely that the results we got could happen just by chance.  We call the results statistically significant if it is “unlikey” that the results could have happened by chance.

More on Significance  Later in the course we will find out how to calculate levels of significance.  p < 0.01 would be acceptable  p < 0.05 would be borderline  p > 0.05 would be suspect.

Significance …  In the studies you are reading you will find “p” numbers given. Those generally have to do with the likelihood that the result could happen by chance. So “p” number less than, say, 0.01 would be acceptable. p < 0.05 would be ok in some circumstances.  Numbers bigger than that are suspect but could still allow the researcher to argue in favor of the result.

Observational Studies  Passive  Collect data  Do calculations  Describe the population studied  Really susceptible to lurking variables.