# Experiments and Observational Studies - PowerPoint PPT Presentation

PPT – Experiments and Observational Studies PowerPoint presentation | free to view - id: 709b06-Yzk4M

The Adobe Flash plugin is needed to view this content

Get the plugin now

View by Category
Title:

## Experiments and Observational Studies

Description:

### Chapter 13 Experiments and Observational Studies Review Population Parameter This is a numerical summary of a population Proportion, mean, standard deviation We don ... – PowerPoint PPT presentation

Number of Views:42
Avg rating:3.0/5.0
Slides: 33
Provided by: mytea46
Category:
Tags:
Transcript and Presenter's Notes

Title: Experiments and Observational Studies

1
Chapter 13
• Experiments and Observational Studies

2
Review
• Population Parameter
• This is a numerical summary of a population
• Proportion, mean, standard deviation
• We dont know this number and usually never will.
• Sample Statistic
• This is a numerical summary computed from a
subset of the population(a sample).
• sample proportion, sample mean, sample standard
deviation
• We know this number because we can compute it. We
use this number to try and get an idea of what
the population parameter(which we dont know)
might be.

3
Observational Study
• Observing data in the wild.
• Researchers don't assign treatments to subjects
they simply observe them.
• Results show association, but do not prove
causation.
• Also called an Investigative Study.
• Sample surveys are observational studies.

4
Observational Studies
• Two types
• Retrospective Study
• Subjects are selected
• Previous behaviors or conditions are determined
• Prospective Study
• Subjects are selected
• Subjects are followed to observe future outcomes

5
Experiments
• A way to prove a cause and effect relationship
between two or more variables
• Manipulate explanatory variable
• Observe response
• In an experiment you are applying a treatment to
the subjects in an observational study you just
observe them

6
Experiment Terminology
• Experimental Units individuals on whom the
experiment is performed
• Factors explanatory variable(s)
• Need at least one factor for every experiment

7
Experiment Terminology
• Levels specific values that the experimenter
chooses for a factor
• Need at least two levels for each factor
• Treatments different levels of a single factor
or combinations of levels of two or more factors

8
Example OptiGro Fertilizer
• An ad for OptiGro plant fertilizer claims that
with this product we will grow juicier, tastier
tomatoes. We'd like to test this claim and see
if we can get by with a half-dose.
• What are the experimental units in this example?
• Tomato plants (the thing we apply the treatment
to)
• What is the response we are interested in?
• Juiciness and Tastiness of the tomatoes

9
Example OptiGro Fertilizer
• What is the factor?
• Fertilizer
• What are the levels of this factor?
• No Fertilizer (to see if the fertilizer even
works)
• Full-Dose
• Half-Dose (want to see if the half dose is as
good as the full)
• What are the treatments?
• No Fertilizer, Full-Dose, Half-Dose

10
Example OptiGro Fertilizer
• The makers of OptiGro want to ensure that their
product will work under a wide variety of
watering conditions. Let's include this as a
second factor specifically lets look at daily
watering and watering every other day.
• How many factors are there now?
• 2
• What are the factor(s)?
• Fertilizer and water
• What are factor levels
• Fertilizer
• no fertilizer, half dose, full dose
• Water
• every day, every other day

11
Example OptiGro Fertilizer
• What are the treatments in the new experiment?
• Half-Dose of fertilizer, daily watering
• Full-Dose, daily watering
• No fertilizer, daily watering
• Half-Dose, water every other day
• Full-Dose, water every other day
• No fertilizer, water every other day

12
Experiment Terminology
• Control Group experimental units assigned to a
baseline treatment level
• Provides basis for comparison
• Baseline either a gold standard or a placebo
• Placebo a null treatment known to have no
effect
• Placebo Effect tendency of many human subjects
to show response even when administered a placebo

13
Experiment Terminology
• Blinding - the researcher disguises treatments
• Single Blind - either the subject or the
evaluator is unaware of the treatment
• Double Blind - both the subject AND the evaluator
are in the dark
• Reduces personal bias

14
Experiment Terminology
• Confounding - A condition where the effects of
two variables on the response cant be
distinguished from each other
• Lurking Variable - A variable that effects the
relationship between the response variable and
the explanatory variable and is not included
among the variables in a study.

15
Principles of Experimental Design
• Control
• Make all conditions as similar as possible for
all treatment groups
• The only difference between groups should be the
treatments
• Controlling outside influences reduces
variation in the responses, making it easier to
detect differences among the groups due to the
treatments

16
Principles of Experimental Design
• Randomization
• Experimental units should be randomly assigned to
treatment groups
• Order of the trials should also be randomized
• Some uncontrolled sources of variation will be
controlled through randomization
• Experiments without randomization may have biased
results

17
Principles of Experimental Design
• Replication
• In an experiment, each treatment is given to
several different experimental units
• Entire experiment should be repeated on a
different group of experimental units

18
Blocking
• This can be considered another principle of
experimental design, but not required in an
experimental design
• Group similar individuals together and then
randomize within each of these blocks
• Reduces the effects of identifiable attributes of
the subjects that cannot be controlled

19
Example
• The leg muscles of men aged 60 to 75 were 50 to
80 stronger after they participated in a
16-week, high-intensity resistance-training
program twice a week.
• Experiment
• Men aged 60 to 75 (experimental units)
• Exercise (1 level) (factors)
• 1 treatment 16-week resistance-training
• Strength levels pre- and post-exercise program
(response)
• Not blinded
• Applies only to men 60 to 75 who participate in
similar exercise programs. (Nature and scope of
conclusion)
• Randomization?

20
Example
• In 2001 a report in the Journal of the American
Cancer Institute indicated that women who work
nights have a 60 chance of developing breast
cancer. Researchers based these findings on the
work histories of 763 women with breast cancer
and 741 women without the disease.
• Observational Study
• Retrospective
• Women unknown selection process with in
formation taken from work histories
• (subjects studied and how they were selected)
• Risk of breast cancer
• parameter of interest
• Observational study, no way to know that working
nights causes breast cancer.
• Nature and scope of the conclusion.

21
Example
• Some gardeners prefer to use non-chemical methods
to control insect pests in their gardens. Two
kinds of traps have been designed and Researchers
want to know which one is more effective. They
randomly choose 10 locations in a large garden
and place one of each kind of trap at each
location. After a week they count the number of
bugs in each trap.
• Experiment.
• Locations in a garden. (experimental units)
• 1 factor traps(2 levels). (factors)
• 2 treatments. ( of treatments)
• Number of bugs in trap. (response variable
measured)
• Blocked by locations. (type of design)
• Not blind.
• One type of trap is more effective than the other
(Nature and scope of conclusion)

22
Diagrams
• One way to portray an experiment is through a
diagram.
• UNITS ? TREATMENT ? RESPONSE

Group 1
Treatment 1
Random Assignment
Compare response
Group 2
Treatment 2
23
Example Shingles
• A research doctor believes a new ointment will be
more effective than the current medication in
treating shingles (a painful skin rash). Eight
patients have volunteered for our study. We will
design an experiment to help the doctor verify
her claim.

24
Example Shingles
• Experimental Unit Person (we have 8)
• Response Severity of shingles
• Factor ointment
• Levels current ointment and new ointment
• Why do we not want a placebo here?
• Treatments current ointment and new ointment
• Treatments are the same as levels here because we
only have one factor

25
Example - Shingles
• Control
• Ointment taken every day for both groups
• All other care is similar
• Replication
• 4 patients in each treatment group
• Randomization
• Randomly assign 4 patients to each treatment
group
• How do we do this?

26
Example - Shingles
• Patients Alex, Bethany, Carl, Denise, Evelyn,
Fred, George, Hannah
• Number the patients 1 through 8
• Use random numbers to assign treatments 41098
18329 78458 31685 55259
• First 4 patients we select get new treatment, the
rest get the current treatment

27
Example - Shingles
• By ones4 1 0 9 8 1 8 3 2 9 7 8 4 5 8 3 1 6 8 5
5 5 2 5 9
• Throw out 0 and 9 (they dont correspond to
patients)
• Throw out repeats
• New ointment 4, 1, 8, 3
• Denise, Hannah, Alex, Carl
• Current ointment 2, 5, 6, 7 (everyone else)
• Bethany, Evelyn, Fred, George

28
Example - Shingles
Group 1 4 patients
Treatment 1 New Ointment
Randomly Assign the 8 patients to groups
Compare severity of shingles
Group 2 4 patients
Treatment 2 Current Ointment
29
Example - Shingles
• Could we make this experiment double-blind?
• Double-blind means that neither the patient nor
the person evaluating the results knows who is
receiving each treatment. To make this
double-blind we would have to assume that the two
ointments look alike same color, unmarked tubes,
similar texture, similar odors, etc.
• If one ointment had a distinctive odor, it would
probably not be possible to make the experiment
double-blind.

30
Example Blocking
• Cancer treatment (3 treatments)
• We suspect that men and women respond differently
so divide subjects into men and women
• Randomly assign each block to three groups
• The three groups in each block receive only one
treatment
• Compare survival rates

31
Examples Blocking
Group 1
Treatment 1
Compare survival rates
Random Assign.
Men
Group 2
Treatment 2
Group 3
Treatment 3
Subjects
Group 1
Treatment 1
Compare survival rates
Random Assign.
Women
Group 2
Treatment 2
Group 3
Treatment 3
32
Statistical Significance
• If the differences between the treatment groups
are big enough, we will attribute the differences
to the treatments
• How big is big enough?