1. 1.3 Experimental Design

2. What is the goal of every statistical Study?  Collect data  Use data to make a decision If the process to collect data is flawed, so is the conclusion.

3. Designing a Statistical Study  Identify the variable of interest and the population.  Develop a detailed plan for collecting data. If using a sample, make sure it represents the population.  Collect data.  Describe the data.  Interpret the data and make decisions about the population.  Identify any possible errors.

4. Data Collection  Do an observational study Observes and measures characteristics of a sample but does not change existing conditions.  Perform an experiment Treatment is applied to part of the group, results are noted Control group is used. No treatment is applied A Placebo may be used.

5. Data Collection(cont.)  Use a simulation Use of a mathematical or physical model to reproduce the conditions of the situation or process studied. Allows study of situations that are difficult or dangerous to reproduce.  Use a survey Ask questions, get answers.

6. Experimental Design  Key Elements Control  Why do we need to control the experiment?  Confounding variable – when you can’t tell the difference between effects of different factors on a variable.  Placebo effect – subject react favorably to placebo. No real treatment given.  Blinding – subject does not know if they have been given a treatment or placebo.  Double-blind – neither the researcher or the subject knows who gets the actual treatment until study is over.

7. Key Elements(cont)  Randomization Process of randomly assigning subjects to different treatment groups. Randomized block design – subjects are classified by similar characteristics then randomly assigned within that group. (pg 20) Matched pair design – subjects paired by similarity. One gets treatment, one gets placebo.

8. Key Elements(cont)  Replication Repetition of the experiment using a large number of subjects. Rule of large numbers  An experiment that is performed a large number of times approaches theoretical results.

9. Sampling WHY?  Population vs. sample  Census or sample  Error – may not be avoidable. Needs to be minimized.  Biased – not representative of the population.

10. Sampling Techniques  Random – every member has an equal chance of being selected. Use tables or random number generators. (A7 in book)  Stratified - when it is important to have members from each segment of the population. Random sample within the smaller strata.

11. Sampling Techniques (Cont)  Cluster – population falls into naturally occurring subgroups. One or more of the subgroups is used.  Systematic – population is ordered and then every ___ is chosen.  Convenience – because they are there. Often leads to biased results.