1. The Practice of Statistics, 5th Edition Starnes, Tabor, Yates, Moore Bedford Freeman Worth Publishers CHAPTER 4 Designing Studies 4.2Experiments

2. Learning Objectives After this section, you should be able to: The Practice of Statistics, 5 th Edition2 DISTINGUISH between an observational study and an experiment. EXPLAIN the concept of confounding. IDENTIFY the experimental units, explanatory and response variables, and treatments in an experiment. EXPLAIN the purpose of comparison, random assignment, control, and replication in an experiment. DESCRIBE a completely randomized design for an experiment. DESCRIBE the placebo effect and the purpose of blinding in an experiment. INTERPRET the meaning of statistically significant in the context of an experiment. EXPLAIN the purpose of blocking in an experiment. DESCRIBE a randomized block design or a matched pairs design for an experiment. Experiments

3. The Practice of Statistics, 5 th Edition3 Observational Study vs. Experiment 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. 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.

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5. 5 Observational Study vs. Experiment 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. To explain confounding you should explain how the variable you chose is associated with the explanatory variable and also how it affects the response variable.

6. The Practice of Statistics, 5 th Edition6 Two variables are confounded if it is impossible to determine which of the two variables is causing a change in the response variable. Consider a study to determine if heavy drinkers die at a younger age. –It is quite possible that the heaviest drinkers come from a different background or social group. Heavy drinkers may be more likely to smoke, or eat junk food, all of which could be factors in reducing longevity. Ice cream sales and drowning –The confounding variable “summer” increases ice cream sales and also increases the number of drownings. Smoking and lung cancer –While smoking may cause lung cancer, it is not the only cause. Asbestos, genetics, cigar smoking, and other thing cuase lung cancer too. Each of those other variables that could cause lung cancer are confounding variables.

7. The Practice of Statistics, 5 th Edition7 AP Exam Tip on p.236 –If you are asked to identify a possible confounding variable in a given setting, you are expected to explain how the variable you choose is associated with the explanatory variable and how it affects the response variable. Ex. – Taking hormones reduces the risk of heart-attacks –The following response would not earn full credit on the AP exam: »“Diet is a confounding variable because people with bad diets are more likely to have a heart attack” –The following response would earn full credit: »Diet is a confounding variable because women who pay attention to their diet are more inclined to be health conscious and also more inclined to take hormone replacement pills. You cannot be sure that the reduction in heat attacks came from the hormone replacement rather than the careful diet.

8. The Practice of Statistics, 5 th Edition8 CYU on p.237

9. The Practice of Statistics, 5 th Edition9 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. 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. 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.

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11. The Practice of Statistics, 5 th Edition11 Often times the explanatory variables in an experiment are called factors if there is more than one explanatory variable. If an experiment has several factors, the combinations of each level of each factor form the treatments.

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13. The Practice of Statistics, 5 th Edition13 http://www.ted.com/talks/michael_norton_how_to_buy_happiness#t- 107021http://www.ted.com/talks/michael_norton_how_to_buy_happiness#t- 107021 –Describes a multifactor experiment to determine if money can buy happiness. –There are 2 factors, amount of money & how it is spent.

14. The Practice of Statistics, 5 th Edition14 How to Experiment Badly Many laboratory experiments use a design like the one in the online SAT course example on p.240: 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.

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16. The Practice of Statistics, 5 th Edition16 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.

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18. The Practice of Statistics, 5 th Edition18 Principles of Experimental Design The basic principles for designing experiments are as follows: 1. Comparison. Use a design that compares two or more treatments. 2. Random assignment. Use chance to assign experimental units to treatments. Doing so helps create roughly equivalent groups of experimental units by balancing the effects of other variables among the treatment groups. 3. Control. Keep other variables that might affect the response the same for all groups. 4. 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. Principles of Experimental Design

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21. The Practice of Statistics, 5 th Edition21 Completely Randomized Design 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. 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. Experimental Units Random Assignment Group 1 Group 2 Treatment 1 Treatment 2 Compare Results

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25. The Practice of Statistics, 5 th Edition25 CYU on p.247

26. The Practice of Statistics, 5 th Edition26 Experiments: What Can Go Wrong? The 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. The 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. 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.

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28. The Practice of Statistics, 5 th Edition28 http://www.cbsnews.com/videos/treating-depression-is-there-a- placebo-effect/http://www.cbsnews.com/videos/treating-depression-is-there-a- placebo-effect/

29. The Practice of Statistics, 5 th Edition29 Inference for Experiments An observed effect so large that it would rarely occur by chance is called statistically significant. A statistically significant association in data from a well-designed experiment does imply causation. An observed effect so large that it would rarely occur by chance is called statistically significant. A statistically significant association in data from a well-designed experiment does imply causation. In an experiment, researchers usually hope to see a difference in the responses so large that it is unlikely to happen just because of chance variation. We can use the laws of probability, which describe chance behavior, to learn whether the treatment effects are larger than we would expect to see if only chance were operating. If they are, we call them statistically significant.

30. The Practice of Statistics, 5 th Edition30 Blocking When a population consists of groups of individuals that are “similar within but different between,” a stratified random sample gives a better estimate than a simple random sample. This same logic applies in experiments. A block is a group of experimental units that are known before the experiment to be similar in some way that is expected to affect the response to the treatments. In a randomized block design, the random assignment of experimental units to treatments is carried out separately within each block. A block is a group of experimental units that are known before the experiment to be similar in some way that is expected to affect the response to the treatments. In a randomized block design, the random assignment of experimental units to treatments is carried out separately within each block. See example on p.251

31. The Practice of Statistics, 5 th Edition31 See example on p.254

32. The Practice of Statistics, 5 th Edition32 Matched Pairs Design A common type of randomized block design for comparing two treatments is a matched pairs design. The idea is to create blocks by matching pairs of similar experimental units. A matched pairs design is a randomized blocked experiment in which each block consists of a matching pair of similar experimental units. Chance is used to determine which unit in each pair gets each treatment. Sometimes, a “pair” in a matched-pairs design consists of a single unit that receives both treatments. Since the order of the treatments can influence the response, chance is used to determine with treatment is applied first for each unit. A matched pairs design is a randomized blocked experiment in which each block consists of a matching pair of similar experimental units. Chance is used to determine which unit in each pair gets each treatment. Sometimes, a “pair” in a matched-pairs design consists of a single unit that receives both treatments. Since the order of the treatments can influence the response, chance is used to determine with treatment is applied first for each unit.

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34. Section Summary In this section, we learned how to… The Practice of Statistics, 5 th Edition34 DISTINGUISH between an observational study and an experiment. EXPLAIN the concept of confounding. IDENTIFY the experimental units, explanatory and response variables, and treatments in an experiment. EXPLAIN the purpose of comparison, random assignment, control, and replication in an experiment. DESCRIBE a completely randomized design for an experiment. DESCRIBE the placebo effect and the purpose of blinding in an experiment. INTERPRET the meaning of statistically significant in the context of an experiment. EXPLAIN the purpose of blocking in an experiment. DESCRIBE a randomized block design or a matched pairs design for an experiment. Experiments