Section 4.3 Using Studies Wisely By: Michelle Rondilla & Alexander Hasson Period 4

Key Terms Most statistical studies aim to make inferences that go beyond the data actually produced. Inference about the population requires that an individual taking part in a study be randomly selected from the larger population. A well designed experiment that randomly assigns treatments to experimental units allow inference about cause and effect. Lack of realism in an experiment can prevent us from generalizing its results.

Scope of Interference Example #1 ▫“The US census bureau carries out a monthly Current Population Survey of about 60,000 households. Their goal is to estimate from these randomly selected data households to estimate the percent of unemployed individuals in the population.” In this example, the random sample avoids bias and provides trustworthy results, which makes it safe to make an “inference about the population.”

Scope of Interference Example #2 ▫“Scientists performed an experiment that randomly assigned 21 volunteer subjects to one of two treatments: sleep deprivation for one night or unrestricted sleep. The experimenters hoped to show that sleep deprivation causes a decrease in performance two days later.” If the unrestricted sleep group performs better than the sleep deprivation group, scientists can conclude that sleep deprivation caused the decrease in performance, thus making it safe to make an “inference about cause and effect”

Were individuals randomly assigned to groups? Were individuals randomly selected? YesNo YesInference about the population: YES Inference about cause and effect: YES Inference about cause and effect: NO NoInference about the population: NO Inference about cause and effect: YES Inference about cause and effect: NO Random selection of individuals allows inference about the population. Random assignment of individuals to groups allows inference about cause and effect.

Challenges of Establishing Causation A well-designed experiment tells us that changes in the explanatory variable cause changes in the response variable. It tells us that this happened for specific individuals in the specific environment of this specific experiment. Lack of Realism can limit our ability to apply the conclusions of an experiment to the settings of greatest interest.

Lack of Realism Example Do those high center brake lights, required on all cars sold in the United States since 1986, really reduce rear-end collisions? When the experiment was carried out, most cars didn’t have the extra brake light. Now that almost all cars have the third light, they no longer capture attention. Lack of Realism

Observational Studies In some cases, it isn’t practical or even ethical to do an experiment. The best data you have about cause-and-effect questions come from observational studies. Example: Does Smoking Cause Lung Cancer? ▫Doctors had long observed that most lung cancer patients were smokers. ▫Observational Study

Criteria for Establishing Causation 1.The association is strong 2.The association is consistent 3.Larger values of the explanatory variable are associated with stronger responses 4.The alleged cause precedes the effect in time 5.The alleged cause is plausible

Homework pg. 269 #102 & #107