1. + Chapter 4 Designing Studies 4.1Samples and Surveys 4.2Experiments 4.3Using Studies Wisely

2. + Section 4.2 Experiments After this section, you should be able to… DISTINGUISH observational studies from experiments DESCRIBE the language of experiments APPLY the three principles of experimental design DESIGN comparative experiments utilizing completely randomized designs and randomized block designs, including matched pairs design Learning Objectives

3. + Experiments Observational Study versus Experiment In contrast to observational studies, experiments don’t just observe individuals or ask them questions. They activelyimpose some treatment in order to measure the response. Definition: An observational study observes individuals and measures variables of interest but does not attempt to influence the responses. An experiment deliberately imposes some treatment on individuals to measure their responses. When our goal is to understand cause and effect, experiments are the only source of fully convincing data. The distinction between observational study and experiment is one of the most important in statistics.

4. + Experiments Observational Study versus ExperimentEXAMPLES In a health study, women who took hormonesseemed to reduce their risk of heart attack by 35%to 50%. The risks of taking hormones appearedsmall compared with benefits.Observational study or Experiment? A study randomly assigned elderly women to eithersoy or placebo to test the proposed benefits of soylike lower rates osteoporosis. The study showedthat soy did not lower rate of osteoporosis.Observational study or Experiment?.

5. + Experiments Observational Study versus Experiment Observational studies of the effect of one variable on another often fail because of confounding between the explanatory variable and one or more lurking variables. Definition: A lurking variable is a variable that is not among the explanatory or response variables in a study but that may influence the response variable. Confounding occurs when two variables are associated in such a way that their effects on a response variable cannot be distinguished from each other. Well-designed experiments take steps to avoid confounding.

6. + Experiments Observational Study versus ExperimentEXAMPLE While experiments showed that soy did not lowerthe risk of osteoporosis in elderly women,observational studies show lower rates ofosteoporosis in Asian cultures in which soy is amajor dietary component. What are potential confounding variables in the observationalstudies?

7. + Experiments The Language of Experiments An experiment is a statistical study in which we actually do something (a treatment ) to people, animals, or objects (the experimental units ) to observe the response. Here is the basic vocabulary of experiments. Definition: A specific condition applied to the individuals in an experiment is called a treatment. If an experiment has several explanatory variables, a treatment is a combination of specific values of these variables. The experimental units are the smallest collection of individuals to which treatments are applied. When the units are human beings, they often are called subjects. Sometimes, the explanatory variables in an experiment are called factors. Many experiments study the joint effects of several factors. In such an experiment, each treatment is formed by combining a specific value (often called a level) of each of the factors.

8. + Experiments The Language of Experiments EXAMPLES A study published in the New England Journal of Medicine (March 11, 2010) compared two medicines to treat head lice: an oral medicationcalled ivermectin and a topical lotion containing malathion.Researchers studied 812 people in 376 households in seven areasaround the world. Of the 185 households randomly assigned toivermectin, 171 were free from head lice after two weeks comparedwith only 151 of the 191 households randomly assigned to malathion. What are the experimental units? What the explanatory & response variables? What are the treatments? A gardener plants 24 similar tomato plants in identical plots in hisgreenhouse. He will add fertilizer to the soil in half the pots. Also, hewill water 8 of the plants with 0.5 gallon of water per day, 8 of theplants with 1 gallon of water per day, and the remaining 8 plants with1.5 gallon of water per day. At the end of three months, he will recordthe total weight of tomatoes produced on each plant. What are the experimental units? What the explanatory & response variables? What are the treatments?

9. + Experiment How to Experiment Badly Experiments are the preferred method for examining the effectof one variable on another. By imposing the specific treatmentof interest and controlling other influences, we can pin downcause and effect. Good designs are essential for effectiveexperiments, just as they are for sampling. Example, page 236 A high school regularly offers a review course to prepare students for the SAT. This year, budget cuts will allow the school to offer only an online version of the course. Over the past 10 years, the average SAT score of students in the classroom course was 1620. The online group gets an average score of 1780. That’s roughly 10% higher than the long- time average for those who took the classroom review course. Is the online course more effective? Students -> Online Course -> SAT Scores

10. + Experiment How to Experiment Badly Many laboratory experiments use a design like the one in theonline SAT course example: Experimental Units Treatment Measure Response In the lab environment, simple designs often work well. Field experiments and experiments with animals or people deal with more variable conditions. Outside the lab, badly designed experiments often yield worthless results because of confounding.

11. + Summary of Statistical Studies How to Analyze & Critique a Statistical Study Ask yourself these questions when you read about a study:1. What is the population of interest? 2. Is this an experiment or observational study? 3. What was the sampling method? SRS? Convenience? Voluntary response? Stratified random sample? Cluster sample? 4. Is the sample likely to be representative of the population of interest? 5. Are there any obvious sources of bias? Undercoverage? Nonresponse? Response bias? Wording of questions? Other? 6. Are there any potential confounding variables?

12. + Summary of Statistical Studies How to Analyze & Critique a Statistical StudyEXAMPLES Many students regularly consume caffeine to help them stayalert. Thus, it seems plausible that taking caffeine mightincrease an individual’s pulse rate. A group of students wantedto test out this theory. At lunch, the asked the 10 otherstudents at their lunch table to participate in their study. Thestudents’ pulse rates were measured, then they drank somecola with caffeine, and after 10 minutes their pulse rates weremeasured again. Analyze & critique this study. 1. What is the population of interest? 2. Is this an experiment or observational study? 3. What was the sampling method? 4. Is the sample likely to be representative of the population of interest? 5. Are there any obvious sources of bias? 6. Are there any potential confounding variables?