agresti/franklin statistics, 1 of 56 section 4.3 what are good ways and poor ways to experiment?
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Section 4.3
What Are Good Ways and Poor Ways to Experiment?
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An Experiment
Assign each subject (called an experimental unit ) to an experimental condition, called a treatment
Observe the outcome on the response variable
Investigate the association – how the treatment affects the response
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Elements of a Good Experiment
Primary treatment of interest
Secondary treatment for comparison
Comparing the primary treatment results to the secondary treatment results help to analyze the effectiveness of the primary treatment
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Control Group
Subjects assigned to the secondary treatment are called the control group
The secondary treatment could be a placebo or it could be an actual treatment
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Randomization in an Experiment
It is important to randomly assign subjects to the primary treatment and to the secondary (control) treatment
Goals of randomization: • Prevent bias
• Balance the groups on variables that you know affect the response
• Balance the groups on lurking variables that may be unknown to you
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Blinding the Study
Subjects should not know which group they have been assigned to – the primary treatment group or the control group
Data collectors and experimenters should also be blind to treatment information
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Example: A Study to Assess Antidepressants for Quitting Smoking
Design:
• 429 men and women
• Subjects had smoked 15 cigarettes or more per day for the previous year
• Subjects were highly motivated to quit
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Example: A Study to Assess Antidepressants for Quitting Smoking
Subjects were randomly assigned to one of two groups:• One group took an antidepressant daily
• Second group did not take the antidepressant (this group is called the placebo group)
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Example: A Study to Assess Antidepressants for Quitting Smoking
The study ran for one year
At the end of the year, the study observed whether each subject had successfully abstained from smoking or had relapsed
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Example: A Study to Assess Antidepressants for Quitting Smoking
Results after 1 year:
• Treatment Group: 55.1% were not smoking
• Placebo Group: 42.3% were not smoking
Results after 18 months:• Antidepressant Group: 47.7% not smoking
• Placebo Group: 37.7% not smoking
Results after 2 years:• Antidepressant Group: 41.6% not smoking
• Placebo Group: 40% not smoking
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Example: A Study to Assess Antidepressants for Quitting Smoking
Question to Think About: Are the differences between the two groups statistically significant or are these differences due to ordinary variation?
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Section 4.4
What Are Other Ways to Conduct Experimental and Observational
Studies?
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Multifactor Experiments
Multifactor Experiments: have more than one categorical explanatory variable (called a factor).
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Example: Do Antidepressants and/or Nicotine Patches Help Smokers Quit?
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Matched-Pairs Design
Each subject serves as a block
Both treatments are observed for each subject
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Example: A Study to Compare an Oral Drug with a Placebo for Treating Migraine Headaches
Subject Drug Placebo
1 Relief No Relief First matched pair
2 Relief Relief
3 No Relief No Relief
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Blocks and Block Designs
Block: collection of experimental units that have the same (or similar) values on a key variable
Block Design: identifies blocks before the start of the experiment and assigns subjects to treatments with in those blocks
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Experiments vs Observational Studies
An Experiment can measure cause and effect
An observational study can yield useful information when an experiment is not practical
An observational study is a practical way of answering questions that do not involve trying to establish causality
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Observational Studies
A well-designed and informative observational study can give the researcher very useful data.
Sample surveys that select subjects randomly are good examples of observational studies.
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Random Sampling Schemes
Simple Random Sample: every possible sample has the same chance of selection
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Random Sampling Schemes
Cluster Random Sample: • Divide the population into a large number
of clusters
• Select a sample random sample of the clusters
• Use the subjects in those clusters as the sample
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Random Sampling Schemes
Stratified Random Sample:• Divide the population into separate groups,
called strata
• Select a simple random sample from each strata
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Observational Studies
Well-designed observational studies use random sampling schemes
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Retrospective and Prospective Studies
Retrospective study: looks into the past
Prospective study: follows its subjects into the future
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Case-Control Study
A case-control study is an observational study in which subjects who have a response outcome of interest (the cases) and subjects who have the other response outcome (the controls) are compared on an explanatory variable
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Example: Case-Control Study
Response outcome of interest: Lung cancer
• The cases have lung cancer
• The controls did not have lung cancer
The two groups were compared on the explanatory variable:• Whether the subject had been a smoker