1. Experimental design

2. Basics of experimental design Experimental units are the individuals on which the experiment is done. Subjects are when the experimental units are human beings. Explanatory variable is the independent variable Response variable is the dependent variable Factors are the explanatory variable in an experiment. Treatments are a specific experimental condition applies to the units.

3. Completely randomized design In a completely randomized design, the treatments are assigned to all the experimental units completely by chance.

4. Example A completely randomized design layout for the Acme Experiment is shown in the table to the right. In this design, the experimenter randomly assigned participants to one of two treatment conditions. They received a placebo or they received the vaccine. The same number of participants (500) were assigned to each treatment condition (although this is not required). The response variable is the number of colds reported in each treatment condition. If the vaccine is effective, participants in the "vaccine" condition should report significantly fewer colds than participants in the "placebo" condition.placebo A completely randomized design relies on randomization to control for the effects of extraneous variables. The experimenter assumes that, on average, extraneous factors will affect treatment conditions equally; so any significant differences between conditions can fairly be attributed to the explanatory variable.randomization

5. Assignment TPS 5.33, 5.35, 5.37, 5.39, 5.40, 5.43

6. Basic Principles of Experimental Design 1.Control for lurking variables that might affect the response: Use a comparative design and ensure that the only systematic difference between the groups is the treatment administered. 2.Random assignment: Use impersonal chance to assign experimental units to treatments. This helps create roughly equivalent groups of experimental units by balancing the effects of lurking variables that aren’t controlled on the treatment groups. 3.Replication: Use enough experimental units in each group so that any differences in the effects of the treatments can be distinguished from chance differences between the groups.