Designing Experiments Purpose for experiments – to study the response of one variable to the changes in other variables. Experimental Units (Subjects) Individuals on which experiment is performed Treatment Applied experimental conditions e.g. aspirin Factor Explanatory variable of an experiment

Designing Experiments Example: Researchers studying the absorption of a drug into the bloodstream inject the drug into 25 people. 30 minutes after the injection, they monitor the concentration of the drug in a subject’s blood. Treatment = Individuals = # of factors = Response variable =

Designing Experiments Comparative experiment Units  Treatment  Response designed in such a way as to eliminate or minimize confounding variables

Designing Experiments Confounding variables can introduce bias into the results of experiments Their influence can be minimized by specific experimental design Randomization of individuals into two groups. Control group – group of individuals that receive sham treatments Placebo group (Placebo – dummy treatment) ½ of individuals Experimental group – group of individual that receive the treatment ½ of individuals

Designing Experiments Assignment into either experimental or control groups can be done via double-blinding where neither the subjects nor the personnel who interact with the subjects know who receives treatment and who receives placebo

Designing Experiments Randomized Comparative Experiment Let impersonal chance assign subjects to groups – Randomization Completely Randomized Designs All experimental units are allocated at random among all treatments Random Assignment Group 1 Treatment Group 2 Placebo Response – compare Results from two groups

Designing Experiments Example: Eye cataracts are responsible for over 40% of blindness around the world. Can drinking tea regularly slow the growth of cataracts? We can’t experiment on people, so we use rats as subjects. Researchers injected 18 young rats with a substance that causes cataracts. One group of the rats also received black tea extract; a second group receive green tea extract; and a third got a placebo. The response variable was the growth of cataracts over the next six weeks. Yes, both tea extracts did slow cataract growth. Outline the design of this experiment.

Designing Experiments Random Allocation of Subjects There will always be some difference outcomes due to chance variation among the subjects

Designing Experiments Observed difference in the response of an experiment must be large enough to assure that it did not arise just by chance When comparing response from each group (placebo and experimental) after the experiment is finished, the difference between the two groups must be large enough so that scientists can be sure that it is arising due to effect of the treatment, and not by chance An effect that is large is called: Statistically Significant

Designing Experiments Principles of Experimental Design 1.Control for confounding variables by comparing various treatments 2.Use randomization in choosing subjects for various experiments 3.Gather enough information (use enough subjects) in each group to reduce chance variation in the results.

MATCHED-PAIRS DESIGNS Compares two treatments Example of block design 2 ways: 1.Chose pairs of subjects closely matched One of the treatments is randomly assigned to one of the subjects 2.use only one individual give both treatments at random Example: Coke taste test: Subjects were given taste of Coke and Pepsi and reported their preference. Whether Coke or Pepsi were offered first was chosen randomly

BLOCK DESIGN BLOCK group of experimental units or subjects that are known before the experiment to be similar in some way that is expected to affect the response to the treatments. Random assignment of units to treatments is carried out separately within each block. Another form of control of lurking variables Draw separate conclusions about each block

Example: Women and men respond differently to advertising. An experiment to compare the effectiveness of three television commercials for the same product will want to look separately at the reactions of men and women, as well as assess the overall response to the adds. A completely randomized design considers all subjects, both men and women, as a single pool. The randomization assigns subjects to three treatment groups without regard to their sex. This ignores the differences between men and women. A better design considers women and men separately. Randomly assign the women to three groups, one to view each commercial. Then separately assign the men at random to three groups.