9DISTINGUISH between an observational study and an experiment. 9EXPLAIN the concept of confounding. 9IDENTIFY the experimental units, explanatory and response variables, and treatments in an experiment. 9EXPLAIN the purpose of comparison, random assignment, control, and replication in an experiment. 9DESCRIBE a completely randomized design for an experiment. 9DESCRIBE the placebo effect and the purpose of blinding in an experiment.

Lesson 4C ~ Experimental Design OBJECTIVES:

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.

EXAMPLE: The article “Coffee Buzz: Study Finds Java Drinkers Live Longer” from the Arizona Daily Star discusses a very large study of coffee drinkers. It suggests that coffee drinkers live a little longer than non-drinkers, whether they drink regular or decaf. Previous studies had indicated that drinking coffee might increase the risk of heart disease, but these studies didn’t take into account that coffee drinkers were also more likely to smoke, drink more alcohol, eat more red meat, and exercise less than non-coffee-drinkers. The new study is still an observational study, however, so we can’t be sure that drinking coffee is the cause of longer life.

Observational studies of the effect of an explanatory variable on a response variable often fail because of confounding between the explanatory variable and one or more other variables. Well-designed experiments take steps to prevent confounding.

Confounding occurs when two variables are associated in such a way that their effects on a response variable cannot be distinguished from each other. We could also say that two variables are confounded if it is impossible to determine which of the two variables is causing a change in the response variable.

Check your understanding: Does reducing screen brightness increase battery life in laptop computers? To find out, researchers obtained 30 new laptops of the same brand. They chose 15 at random and adjusted their screens to the brightest setting. The other 15 laptop screen were left at the default setting of moderate brightness. The researchers then measured how long each machine’s battery lasted.  Was this an observational study or an experiment?

Check your understanding: Does eating dinner with their families improve students’ academic performance?  According to an ABC News article, “Teenagers who eat with their families at least five times a week are more likely to get better grades in school.”  This finding was based on a sample survey conducted by researchers at Columbia University. A) Was this an observational study or an experiment?

B) What are the explanatory and response variables?

C) Explain why such a study cannot establish a cause-and-effect relationship. Suggest a variable that may be confounded with whether families eat dinner together.

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.

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.

Example: A study published in the New England Journal of Medicine (March 11, 2010) compared two medicines to treat head lice: and oral medication called ivermectin and a topical lotion containing malathion. Researchers studied 812 people in 376 households in 7 areas of the world. Of the 185 households randomly assigned to ivermectin, 171 were free from head lice after 2 weeks compared with only 151 of the 191 households randomly assigned to malathion. Identify the experimental units, explanatory and response variables, and the treatments in the experiment.

Sometimes the explanatory variables in an experiment are called factors. Many experiments study the joint effect of several factors. In this case, the treatment is formed by combining a specific value (called a level) of each of the factors. Example: A gardener wants to determine if adding fertilizer or increasing water will affect the productivity of his tomato plants. He plants 24 tomato plants in identical pots in his greenhouse. Half of the pots will receive extra fertilizer. In addition, he will water 8 of the plants with 0.5 gallons of water per day, 8 of the plants with 1 gallon of water, and the remaining 8 with 1.5 gallons of water. At the end of 3 months, he will record the total weight of the tomatoes produced by each plant. What are the experimental units? What are the explanatory and response variables? What are the factors and levels of the treatments? What are the treatments?

HOW TO EXPERIMENT BADLY Many laboratory experiments use a design like the one in the online SAT course example:

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.

HOW TO EXPERIMENT WELL: The remedy for confounding is to perform a comparative experiment in which some units receive one treatment and similar units receive another. Most well designed experiments compare two or more treatments. Comparison alone isn’t enough, if the treatments are given to groups that differ greatly, bias will result. The solution to the problem of bias is random assignment.

In an experiment, random assignment means that experimental units are assigned to treatments using a chance process.

In a completely randomized design, the treatments are assigned to all the experimental units completely by chance. Some experiments may include a control group that receives an inactive treatment or an existing baseline treatment.

Example: Psychologists want to know how different types of television shows impact young children. They recruit 60 four-year-olds and have them watch 9 minutes of a fast-paced children’s program (defined as scene changes every 10-15 seconds), watch 9 minutes of a slow-paced children’s program (changes every 30-45 seconds), or draw for 9 minutes. After the 9 minutes, each child will complete several tasks, including test for mental ability and impulse control. Describe a completely randomized design for this experiment. Make an outline and write a few sentences describing your design.

Using a control group: Our designs should always include some type of a control group, but the psychologist’s in the last example didn’t use one.  Why?  What did they do instead?

WHAT CAN GO WRONG? The logic of a randomized comparative experiment depends on our ability to treat all the subjects the same in every way except for the actual treatments being compared. Good experiments, therefore, require careful attention to details to ensure that all subjects really are treated identically. The most common issue we see occurs when we are using our control group. Sometimes the subject believes that they are receiving the actual treatment, and they respond to it, even though it’s not really doing anything (like a child’s boo-boo becoming cured when their mom kisses it better). This response to a dummy treatment is called the placebo effect. In a double-blind experiment, neither the subjects nor those who interact with them and measure the response variable know which treatment a subject received.

Homework:

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