# Introduction to Studies_ Experiments_ and Simulations

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```					Experiments

AP Statistics Presentation 3.7
Experiments vs. Observational Studies

• Experiments             • Observational Studies
• Observe responses to    • Observe responses to
variables                 variables
• Administer a            • Simply observes
treatment in order to     responses, no
observe the response      attempt to influence
to the treatment          them
• Can determine           • Can NOT determine
causation                 causation (only
correlation)
Causation
• This may be the most significant reason for conducting
an experiment as opposed to an observational study.
• Most often, people are interested in causation.
• For example:
– Will this drug cause my headache to go away?
– Does sending my child to daycare cause my child to be behind
later in life?
– Does making students pass a test to graduate cause
improvement in our nation’s education?
– Will invading Iraq cause long-term peace in that country?
• Unfortunately, experiments (which must control for other
variables) are simply not possible to conduct.
• When it is possible, it is the way to go!
Experiments and Terminology
• Experimental Units
– The things that the experiment is done on
– Also called subjects (when they are human)
• Treatment
– What is actually done to the experimental units
• Factors
– The different types of treatments (which are the
explanatory variables)
• Levels
– Differing amounts of the treatment or factors
Experiments and terminology
• How do the terms apply?
• Consider the experiment:
– A consumer advocacy group is curious about the
effectiveness of pain medication in treating migraine
headaches. They randomly give different doses of
aspirin, tylenol, and ibuprofen to migraine sufferers.
They then measure the results and compare.
• Apply the terms experimental unit, treatment,
factor and level to the scenario above.
Experiments and terminology
• What were the experimental units?
– The migraine patients (since human, subjects)
• What was the treatment?
– The pain medication
• What were the factors?
– The aspirin, tylenol, and ibuprofen
• What were the levels?
– The dosages of the drugs
More terminology
• Just like bivariate data, there is typically an explanatory
variable and a response variable in an experiment.
• Consider the scenario:
– A teacher wants to see if a new computer program can more
effectively increase the reading ability of students than a
traditional classroom setting. She first tests each student from a
4th grade class. She then randomly selects half of the class to
participate in the computer program and the other half in the
traditional curriculum. At the end of the year, she tests the
students reading ability again and compares the results.
• Identify the explanatory variable.
– Whether they received the computer program or traditional
curriculum
• Identify the response variable.
– The difference in the results of the reading ability tests.
Comparative Experiments
• Most experiments are comparative.
• That is, the purpose of the experiment is to
compare a treatment to a lack of treatment
or to compare two or more treatments.
Experimental Design Overview
• Simple Experiments (3 models)
Treatment                                Results

Treatment #1
Treatment #2                     Results & Differences
Treatment #3

Response Variable      Treatment            Response Variable
Nature of Experiments
• The nature of an experiment is to focus in
on causation.
• This is done by controlling variables.
• Variables are controlled through
randomization and the use of control
groups.
Completely Randomized
Experiment
• A graphical model is typically used for
designing experiments.
• Consider the question, “Does smoking
cause lung cancer?”
• Unfortunately, a direct experiment would
be unethical, but here is what it would look
like.
Smoking and Lung Cancer
• Start with, say, 400 volunteers who have never smoked
before.
• Randomly choose 200 of them for the experimental
group and the other 200 form the control group.
• The treatment is to smoke 1 pack of cigarettes per day.
• The control group does not smoke at all.
• Then, track the volunteers for, say 40 years.
• At the end of the 40 years, count up how many in each
group developed lung cancer.

• This is a good description of the experiment, the next
slide shows the same thing in diagram form.
Smoking and Lung Cancer

Experimental Group (200)
Measure Data
Smoke 1 pack of cigarettes
Randomly Allocate

Did lung cancer develop?
per day for 40 years

400 Volunteers                                                                           Observe Results
Draw Conclusions

Control Group (200)              Measure Data
No smoking for 40 years     Did lung cancer develop?
Experiments
• This concludes this presentation.

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