Warm Up

1
Warm Up

• Chapter 3 and 4

2
Which percent confidence do we use in this
course?

• 90
• 50
• 95

3
To reduce bias we

• Increase the sample size.
• Take a simple random sample.
• Cry about it.

4
Which of the following is a random sampling error?

• Processing error
• Response error
• Margin of error
• Convenience sampling error

5
Ideally what is the sampling frame?

• Sample
• Population
• Phone book

6
Chapter 5

• Experiments Good and Bad

7
Components of an Experiment

• Recall that experimental studies are
  characterized by an active intervention to impose
  some treatment. All experiments and many
  observational studies are interested in the
  effect one variable has on another
• Response Variable a variable that measures the
  outcome of the experiment (i.e. the Y variable in
  an equation)
• Explanatory variable the variable that we think
  explains or causes the changes in the response
  variable (i.e. the X variable in an equation)
• Subjects individuals studied in an experiment
• Treatment A specific experimental condition
  applied to the subjects. A treatment can be a
  combination of specific values of explanatory
  variables.

8
EXAMPLE

• Does nicotine gum reduce cigarette smoking?
• Response variable cigarettes smoked
• Explanatory variable nicotine gum
• Does exercise help with weight loss?
• Response variable
• Explanatory variable
• NOTE Explanatory variable is also referred to as
  independent variable and the response variable is
  also referred to as dependent variable.

9
Example

• An administrator at the university wants to know
  if drinking different beverages in the morning
  affects the students performance in class. He
  decides to explore this situation. He randomly
  assigns one group of 45 students to drink orange
  juice every morning for one semester and another
  group of 45 to drink coffee every morning. He
  records their final grade in their earliest class
  at the end of the study.
• Who are the subjects?
• What is the explanatory variable?
• What are the treatments?
• What is the response variable?

10
Hidden Components

• Lurking Variable
• a variable that has an important effect on the
  relationship between the variables in a study but
  is not one of the explanatory variables studied.
• Confounded variable
• We say that two variables are confounded when
  their effects on a response variable cannot be
  distinguished from each other.
• the confounded variables may be explanatory or
  lurking variables.

11
Example

• A study is done to compare the progress of
  students taking a course online versus taking the
  course in the classroom. In order to measure the
  progress of students, a test is given to the
  students after taking the course and the test
  scores are compared.
• Explanatory variable course setting (online vs.
  classroom)
• Response variable test score after course
• Lurking variable Student preparation
• Confounded variables course setting and
  Student preparation

12
An article in a womens magazine says that women
who nurse their babies feel warmer towards their
infants than those that bottle-feed. However,
women choose whether to nurse or bottle-feed.
What is the explanatory variable?

• The way women feel about their babies
• Whether the women bottle-feed or nurse.
• The prior attitude of the women towards their baby

13
An article in a womens magazine says that women
who nurse their babies feel warmer towards their
infants than those that bottle-feed. However,
women choose whether to nurse or bottle-feed.
What is the response variable?

• The way women feel about their babies
• Whether the women bottle-feed or nurse.
• The prior attitude of the women towards their baby

14
An article in a womens magazine says that women
who nurse their babies feel warmer towards their
infants than those that bottle-feed. However,
women choose whether to nurse or bottle-feed.
What is the lurking variable?

• The way women feel about their babies
• Whether the women bottle-feed or nurse.
• The prior attitude of the women towards their baby

15
Randomized Experimental Design

• In an experiment we want to make conclusions
  about the effects of our explanatory variable.
• Randomization is important so that we do not have
  biased groups before we apply the treatments
• The comparative design of the experiment allows
  us to eliminate some of the confounding effects
  that may be present. This allows us to make
  conclusions about the effects of the explanatory
  variables on the response variables.
• Sample size is always important! Use a large
  enough sample!

16
Randomized comparative experiments

• Best way to conduct an experiment is to compare
  two or more treatments.
• Reasons Shows us the effect of the explanatory
  variable(s) on the response variable(s) by
  eliminating a great deal of confounding.
• This is exactly what randomized comparative
  experiments do.
• To conduct a randomized comparative experiment,
  we use random assignments to divide our subjects
  into groups. Each group receives a different
  treatment. (We should try to keep our groups
  about the same size.)

17
Example

• Simplest randomized comparative experiment
  divide subjects into two groups.
• Control Group usually the group receiving the
  placebo
• Treatment Group the group actually receiving
  the treatment
• Additional types of randomized comparative
  experiments
• Type 1 Use an existing treatment as the control
  group. Compare this group with a group receiving
  a new treatment. (Example Is medicine A better
  than medicine B?)
• Type 2 Compare multiple new treatments by using
  multiple groups. (Example Which works betters
  medicine A, medicine B, medicine C or medicine D?
  there is No control group.)

18
Common Experiments

• Clinical Trials
• Clinical trials experiments that study the
  effectiveness of medical treatments on actual
  patients
• Placebo a dummy treatment that is made to look
  exactly like the true treatment (There are no
  active ingredients in a placebo.)
• (If the placebo is a pill, sometimes it is
  referred to as a sugar pill.)
• Main Reason to use a placebo Eliminate the
  placebo effect
• Placebo effect Positive response to a dummy
  treatment.
• The idea of receiving a treatment can affect the
  outcome of a clinical trial.

19
Example

• In order to study the effectiveness of vitamin C
  in preventing colds, a researcher recruited 200
  volunteers. She randomly assigned 100 of them to
  take vitamin C for 10 weeks and the remaining 100
  to take a placebo. The 200 participants recorded
  how many colds they had during the 10 weeks. The
  two groups were compared, and the researcher
  announced that taking vitamin C reduces the
  frequency of colds.

20
Logic of experimental design

• Randomized comparative experiments are one of the
  most important ideas in statistics.
• They give us the opportunity to draw
  cause-and-effect conclusions.
• How do they accomplish this goal?
• By using randomization, we are able to form
  groups that are similar in all respects before we
  apply the treatment.
• By using comparative designs, influences other
  than the experimental treatment(s) operate
  equally on all groups.
• By using this type of design, it is easy to see
  that differences in the response variables must
  be due to the effects of the treatments.

21
Principles of design of experiments

• Control the effect of lurking variables on the
  responses, by comparing two or more treatments.
• Randomize- use impersonal chance to assign
  subjects to treatments so that we do not have
  biased groups before we apply the treatments
• Use enough subjects - reduces chance variation
  in the results

22
Statistically Significant

• To conclude treatments are different, the
  differences in the treatments must be large
  enough to conclude that they do come from the
  variation that is always present in an
  experiment.
• Statistically significant - when an observed
  effect is so large that it would rarely occur by
  chance.
• We will discuss it more fully in later chapters.

23
Observational Studies

• There are many cause-and-effect questions we
  would like to have answers to, but we cant
  always perform experiments
• Examples
• Do cell phones cause drivers to have more
  accidents?
• Does smoking cause lung cancer?
• To get answers to these and other questions, we
  will need to perform observational studies.

24
Observational Studies

• Even though observational studies are not as good
  as experiments, they can still be helpful if they
  follow some basic guidelines.
• Comparative Observational studies should also
  try to compare groups. The difference is that
  the groups cannot be randomly assigned.
• Matching This can be used in conjunction with
  comparison. Here, we try to match up a person in
  one group with a person in another group who has
  similar characteristics. (i.e. age, gender,
  education, number of children, and other possible
  lurking variables.)
• NOTE Comparison Matching cannot eliminate
  confounding completely.
• Solution Measure and adjust for confounding
  variables.

25
Example

• This example refers to a study that length of
  life is explained by church attendance. However,
  it is found that churchgoers were more likely to
  be nonsmokers, physically active, and at their
  right weight. Before drawing any conclusions the
  study must measure and adjust for these
  confounding variables.
• Final Remarks
• Randomization is able to create groups that are
  similar in ALL variables (known and unknown).
• Matching and adjustment are only able to work
  with variables that the study thought to measure.
  If a lurking variable is not thought of
  beforehand, observational studies cannot handle
  them.

26
Homework

• Chapter 4
• Suggested problems
• 1,2, 3, 4, 7, 9, 11, 12, 15, 17, 18, 19, 20,
  21, 23, 25, 27, 29
• To turn in
• 2,4, 12, 18, 20
• Chapter 5
• Suggested Problems
• 1, 2, 3, 5, 7, 9, 10, 11, 13, 15, 16, 17, 18, 19,
  21, 23
• To turn in
• 2, 5, 10,16,18
• Chapter 6
• Suggested Problems
• 1, 2, 3, 4, 5, 7, 9, 10, 11, 13, 15, 16, 17, 19,
  20, 21
• To turn in
• 1, 2, 10, 13,16