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					Chapter 4
Gathering Data




                 1
Looking Back
   In Chapters 2 & 3 we learned how to describe data
    both graphically and numerically.
   For these statistical analyses to be useful, we must
    have good data.
   In fact, the way a study is designed (how we gather
    data) can have a major impact on the results of the
    study.
   The purpose of this course is for you to learn what
    you can conclude about an entire population given a
    sample from that population.
   If a study is poorly designed and implemented, the
    results may be meaningless or misleading.
                                                       2
Two Scenarios
   Study 1
       A U.S. study (2000) compared 469 patients with brain
        cancer to 422 patients who did not have brain cancer. The
        patients’ cell phone use was measured using a
        questionnaire. The two groups’ use of cell phones was
        similar.
   Study 2
       An Australian study (1997) conducted a study with 200
        transgenic mice. One hundred were exposed for two 30
        minute periods a day to the same kind of microwaves with
        roughly the same power as the kind transmitted from a cell
        phone. The other 100 mice were not exposed. After 18
        months, the brain tumor rate for the exposed mice was
        twice as high as that for the unexposed mice.

                        Example taken from Statistics: The Art and   3
                             Science of Learning from Data
Questions to Consider
   How do the two studies differ?
       Study 1




       Study 2




                                     4
Questions to Consider
   How do the two studies differ?
       Study 1
           No treatments assigned
           Patients merely questioned


       Study 2




                                         5
Questions to Consider
   How do the two studies differ?
       Study 1
           No treatments assigned
           Patients merely questioned


       Study 2
           Uses mice in hopes of generalizing to humans




                                                           6
Questions to Consider
   Why do the results of different medical
    studies sometimes disagree?



   Could the second study be performed on
    human beings?



                                              7
Questions to Consider
   Why do the results of different medical
    studies sometimes disagree?
       Differing types of studies, data collection or
        sample frames
   Could the second study be performed on
    human beings?




                                                         8
Questions to Consider
   Why do the results of different medical
    studies sometimes disagree?
       Differing types of studies, data collection or
        sample frames
   Could the second study be performed on
    human beings?
       No, because it would be unethical to knowingly
        expose humans to possibly harmful waves.


                                                         9
Questions to Consider
   Suppose a friend recently diagnosed with
    brain cancer was a frequent cell phone user.
    Is this strong evidence that frequent cell
    phone use increases the likelihood of getting
    brain cancer?
       Informal observations of this type are called
        _____________ _____________.
       You should rely on reputable research studies,
        not anecdotes.

                                                         10
Questions to Consider
   Suppose a friend recently diagnosed with
    brain cancer was a frequent cell phone user.
    Is this strong evidence that frequent cell
    phone use increases the likelihood of getting
    brain cancer?
       Informal observations of this type are called
        anecdotal evidence.
       You should rely on reputable research studies,
        not anecdotes.

                                                         11
Two Main Ways to Gather Data
   Observational Study
       The researcher observes values of the response and
        explanatory variables for the sampled subjects without
        imposing any treatments
       Example:
   Experiment
       The researcher assigns experimental conditions (also
        called treatments) to subjects (also called experimental
        units) and then observes outcomes on the response
        variable.
       Treatments correspond to values of the explanatory
        variable
       Example:

                                                                   12
Two Main Ways to Gather Data
   Observational Study
       The researcher observes values of the response and
        explanatory variables for the sampled subjects without
        imposing any treatments
       Example: Study 1
   Experiment
       The researcher assigns experimental conditions (also
        called treatments) to subjects (also called experimental
        units) and then observes outcomes on the response
        variable.
       Treatments correspond to values of the explanatory
        variable
       Example:

                                                                   13
Two Main Ways to Gather Data
   Observational Study
       The researcher observes values of the response and
        explanatory variables for the sampled subjects without
        imposing any treatments
       Example: Study 1
   Experiment
       The researcher assigns experimental conditions (also
        called treatments) to subjects (also called experimental
        units) and then observes outcomes on the response
        variable.
       Treatments correspond to values of the explanatory
        variable
       Example: Study 2

                                                                   14
Advantages of Experiments
over Observational Studies
   In an observational study, there can always be
    lurking variables affecting the results.
   This means that observational studies can
    _________ show causation.
   It is easier to adjust for lurking variables in an
    experiment.
   In general, we can study the effect of an explanatory
    variable on a response variable more accurately
    with an experiment than with an observational study.

                                                        15
Advantages of Experiments
over Observational Studies
   In an observational study, there can always be
    lurking variables affecting the results.
   This means that observational studies can never
    show causation.
   It is easier to adjust for lurking variables in an
    experiment.
   In general, we can study the effect of an explanatory
    variable on a response variable more accurately
    with an experiment than with an observational study.

                                                        16
Disadvantages of Experiments
   They can be ____________ to perform on the
    subjects in which you are interested.
   It can be difficult to monitor subjects to ensure that
    they are doing what they are told.
   They can take many years, even decades, to
    complete.
   Results of experiments that use animals do not
    ______________ to humans.
   They are unnecessary when the question of interest
    does not involve trying to assess _____________.
                                                         17
Disadvantages of Experiments
   They can be unethical to perform on the subjects in
    which you are interested.
   It can be difficult to monitor subjects to ensure that
    they are doing what they are told.
   They can take many years, even decades, to
    complete.
   Results of experiments that use animals do not
    ______________ to humans.
   They are unnecessary when the question of interest
    does not involve trying to assess _____________.
                                                         18
Disadvantages of Experiments
   They can be unethical to perform on the subjects in
    which you are interested.
   It can be difficult to monitor subjects to ensure that
    they are doing what they are told.
   They can take many years, even decades, to
    complete.
   Results of experiments that use animals do not
    generalize to humans.
   They are unnecessary when the question of interest
    does not involve trying to assess _____________.
                                                         19
Disadvantages of Experiments
   They can be unethical to perform on the subjects in
    which you are interested.
   It can be difficult to monitor subjects to ensure that
    they are doing what they are told.
   They can take many years, even decades, to
    complete.
   Results of experiments that use animals do not
    generalize to humans.
   They are unnecessary when the question of interest
    does not involve trying to assess causality.
                                                         20
Example 4.1
   A large study of student drug use and how it
    depends on drug testing enrolled 76,000 middle and
    high school students. Each student in the study
    filled out a questionnaire. One question asked
    whether the student used drugs. The study found
    that drug use was not affected by student drug
    testing.

   This is an example of an

   Could there be any lurking variables?

                    Example taken from Statistics: The Art and   21
                         Science of Learning from Data
Example 4.1
   A large study of student drug use and how it
    depends on drug testing enrolled 76,000 middle and
    high school students. Each student in the study
    filled out a questionnaire. One question asked
    whether the student used drugs. The study found
    that drug use was not affected by student drug
    testing.

   This is an example of an observational study.

   Could there be any lurking variables?

                    Example taken from Statistics: The Art and   22
                         Science of Learning from Data
Example 4.1
   A large study of student drug use and how it
    depends on drug testing enrolled 76,000 middle and
    high school students. Each student in the study
    filled out a questionnaire. One question asked
    whether the student used drugs. The study found
    that drug use was not affected by student drug
    testing.

   This is an example of an observational study.

   Could there be any lurking variables?
       Frequency of drug testing, whether testing is random, etc.

                         Example taken from Statistics: The Art and   23
                              Science of Learning from Data
Example 4.2
   A researcher buys seeds of two different varieties of
    corn. He randomly selects 30 seeds of each variety
    and plants them in his backyard, making sure to
    label the location of each seed and its type. He then
    measures how long it takes each seed to sprout. At
    the end of the study he compares the average
    germination time of the different varieties.

   This is an example of an

   Could there be any lurking variables?

                     Used with permission from Dr. Ellen Toby   24
Example 4.2
   A researcher buys seeds of two different varieties of
    corn. He randomly selects 30 seeds of each variety
    and plants them in his backyard, making sure to
    label the location of each seed and its type. He then
    measures how long it takes each seed to sprout. At
    the end of the study he compares the average
    germination time of the different varieties.

   This is an example of an experiment.

   Could there be any lurking variables?

                     Used with permission from Dr. Ellen Toby   25
Example 4.2
   A researcher buys seeds of two different varieties of
    corn. He randomly selects 30 seeds of each variety
    and plants them in his backyard, making sure to
    label the location of each seed and its type. He then
    measures how long it takes each seed to sprout. At
    the end of the study he compares the average
    germination time of the different varieties.

   This is an example of an experiment.

   Could there be any lurking variables?
       Soil quality, temperature

                          Used with permission from Dr. Ellen Toby   26
Example 4.3
   A researcher has seeds of only one variety of tomato. She has
    60 nearly identical pots of soil and plants one tomato seed in
    each. She randomly selects 30 pots and keeps them at 75° F.
    The other 30 pots she keeps at 65° F. Aside from temperature,
    she provides the same growing conditions to all pots. She then
    measures how long it takes for the seeds to sprout. At the end of
    the study she compares the average germination time of the
    different temperature groups.

   This is an example of an

   Are there any lurking variables?


                         Used with permission from Dr. Ellen Toby   27
Example 4.3
   A researcher has seeds of only one variety of tomato. She has
    60 nearly identical pots of soil and plants one tomato seed in
    each. She randomly selects 30 pots and keeps them at 75° F.
    The other 30 pots she keeps at 65° F. Aside from temperature,
    she provides the same growing conditions to all pots. She then
    measures how long it takes for the seeds to sprout. At the end of
    the study she compares the average germination time of the
    different temperature groups.

   This is an example of an experiment.

   Are there any lurking variables?


                         Used with permission from Dr. Ellen Toby   28
Example 4.3
   A researcher has seeds of only one variety of tomato. She has
    60 nearly identical pots of soil and plants one tomato seed in
    each. She randomly selects 30 pots and keeps them at 75° F.
    The other 30 pots she keeps at 65° F. Aside from temperature,
    she provides the same growing conditions to all pots. She then
    measures how long it takes for the seeds to sprout. At the end of
    the study she compares the average germination time of the
    different temperature groups.

   This is an example of an experiment.

   Are there any lurking variables?
       No, everything has been controlled here.

                           Used with permission from Dr. Ellen Toby   29
Types of Observational
Studies
   Retrospective
       Observational studies that look back in time
           This is sometimes done to find risk factors for certain
            diseases
   Cross-Sectional
       Observational studies that take a cross section of
        the population at the current time
   Prospective
       Observational studies in which subjects are
        followed into the future
                                                                      30
Sampling Designs for
Observational Studies
   Simple Random Sampling (SRS)
       A simple random sample of n subjects from a
        population is one in which each possible sample
        of that size has the _______ chance of being
        selected.




                                                          31
Sampling Designs for
Observational Studies
   Simple Random Sampling (SRS)
       A simple random sample of n subjects from a
        population is one in which each possible sample
        of that size has the same chance of being
        selected.




                                                          32
Sampling Designs for
Observational Studies
   Stratified Sampling
       A stratified random sample divides the population
        into separate groups, called strata, and then
        selects an SRS of _________ from each stratum.




                                                        33
Sampling Designs for
Observational Studies
   Stratified Sampling
       A stratified random sample divides the population
        into separate groups, called strata, and then
        selects an SRS of subjects from each stratum.




                                                        34
Sampling Designs for
Observational Studies
   Cluster Sampling
       A cluster random sample can be used if the target
        population naturally divides into groups, each of which is
        representative of the entire target population. In this
        method, a SRS of ________(or strata) is taken. Every
        member of the selected groups is put into the sample.




                                                                     35
Sampling Designs for
Observational Studies
   Cluster Sampling
       A cluster random sample can be used if the target
        population naturally divides into groups, each of which is
        representative of the entire target population. In this
        method, a SRS of groups (or strata) is taken. Every
        member of the selected groups is put into the sample.




                                                                     36
Sampling Designs for
Observational Studies
   Systematic Sampling
       A systematic sample selects every kth person from
        the sample frame. The researcher randomly
        selects a number between 1 and k in order to
        know which person to select first, then selects
        every kth person after this.




                                                        37
Advantages of the Various
Sampling Designs
   Simple Random Sampling (SRS)
       It is the easiest most widespread form of
        sampling.
       Each subject has an _______ chance to be in the
        sample.
       The sample enables us to determine how likely it
        is that descriptive statistics (like the sample mean)
        fall close to corresponding values for which we
        would like to make inference (like the population
        mean).
                                                            38
Advantages of the Various
Sampling Designs
   Simple Random Sampling (SRS)
       It is the easiest most widespread form of
        sampling.
       Each subject has an equal chance to be in the
        sample.
       The sample enables us to determine how likely it
        is that descriptive statistics (like the sample mean)
        fall close to corresponding values for which we
        would like to make inference (like the population
        mean).
                                                            39
Advantages of the Various
Sampling Designs
   Stratified Sampling
       It ensures that there are enough _________ in
        each group that you want to compare.
   Cluster Sampling
       It does not require a sampling frame of subjects.
       It is less ___________ to implement.




                                                            40
Advantages of the Various
Sampling Designs
   Stratified Sampling
       It ensures that there are enough subjects in each
        group that you want to compare.
   Cluster Sampling
       It does not require a sampling frame of subjects.
       It is less ___________ to implement.




                                                            41
Advantages of the Various
Sampling Designs
   Stratified Sampling
       It ensures that there are enough subjects in each
        group that you want to compare.
   Cluster Sampling
       It does not require a sampling frame of subjects.
       It is less expensive to implement.




                                                            42
Bias in Sampling
   A sampling method is _________ if
       The sample tends to favor some parts of the
        population over others.
       In other words, the results from the sample are
        not representative of the population.
   Obviously, __________ samples are our
    goal.



                                                          43
Bias in Sampling
   A sampling method is biased if
       The sample tends to favor some parts of the
        population over others.
       In other words, the results from the sample are
        not representative of the population.
   Obviously, __________ samples are our
    goal.



                                                          44
Bias in Sampling
   A sampling method is biased if
       The sample tends to favor some parts of the
        population over others.
       In other words, the results from the sample are
        not representative of the population.
   Obviously, unbiased samples are our goal.




                                                          45
Types of Bias
   Undercoverage
       Occurs when a sampling frame leaves out some groups in
        the population
   Nonresponse bias
       Occurs when some sampled subjects cannot be reached,
        refuse to participate or fail to answer some questions
   Response bias
       Occurs when the subject gives an incorrect response or
        when the question wording or the way the interviewer asks
        the questions is confusing or misleading

                                                                 46
Examples of Poor Samples
that Result in Bias
   Convenience Samples



   Voluntary Response Samples




                                 47
Examples of Poor Samples
that Result in Bias
   Convenience Samples
       Sampling friends
       Sampling at the mall
   Voluntary Response Samples




                                 48
Examples of Poor Samples
that Result in Bias
   Convenience Samples
       Sampling friends
       Sampling at the mall
   Voluntary Response Samples
       Internet surveys
       Call-in surveys




                                 49
Example 4.4
   In 1997 in her book Women and Love, Shere Hite
    presented results of a survey mailed to 100,000
    women in the United States. One of her
    conclusions was that 70% of women who had been
    married at least five years have extramarital affairs.
    She based this conclusion on the replies of only
    4500 women.

   This is an example of


                     Example taken from Statistics: The Art and   50
                          Science of Learning from Data
Example 4.4
   In 1997 in her book Women and Love, Shere Hite
    presented results of a survey mailed to 100,000
    women in the United States. One of her
    conclusions was that 70% of women who had been
    married at least five years have extramarital affairs.
    She based this conclusion on the replies of only
    4500 women.

   This is an example of nonresponse bias.


                     Example taken from Statistics: The Art and   51
                          Science of Learning from Data
Example 4.5
   Ann Landers asked readers, “If you had it to do over
    again, would you have children?” A few weeks later,
    her column was headlined, “70% OF PARENTS
    SAY KIDS NOT WORTH IT.” Of the nearly 10,000
    parents who wrote in, 70% said they would not have
    children if they could go back in time.

   This is an example of ______________________
    sampling.


                     Used with permission from Dr. Ellen Toby   52
Example 4.5
   Ann Landers asked readers, “If you had it to do over
    again, would you have children?” A few weeks later,
    her column was headlined, “70% OF PARENTS
    SAY KIDS NOT WORTH IT.” Of the nearly 10,000
    parents who wrote in, 70% said they would not have
    children if they could go back in time.

   This is an example of voluntary response sampling.



                     Used with permission from Dr. Ellen Toby   53
Example 4.6
   In 1936, the Literary Digest conducted a poll to
    predict the winner of the presidential election. Alf
    Landon and Franklin Roosevelt were both running
    for president. The sample frame for the poll was
    constructed from telephone directories, country club
    memberships and automobile registrations. The
    Digest predicted that Landon would win, but in
    reality FDR won by a landslide.

   This is an example of _____________ sampling that
    resulted in _______________.
                    Example taken from Statistics: The Art and   54
                         Science of Learning from Data
Example 4.6
   In 1936, the Literary Digest conducted a poll to
    predict the winner of the presidential election. Alf
    Landon and Franklin Roosevelt were both running
    for president. The sample frame for the poll was
    constructed from telephone directories, country club
    memberships and automobile registrations. The
    Digest predicted that Landon would win, but in
    reality FDR won by a landslide.

   This is an example of convenience sampling that
    resulted in undercoverage.
                    Example taken from Statistics: The Art and   55
                         Science of Learning from Data
Example 4.7
   An experiment involving adolescent males (ages 15-
    19) appeared in Science, 1995. The purpose of the
    study was to determine whether there was an
    association between survey techniques and the
    desire to give socially acceptable answers.
   The participants were randomly assigned to one of
    two different survey forms, each of which had
    identical questions concerning sexual practices and
    drug habits.



                    Used with permission from Dr. Ellen Toby   56
Example 4.7
   The two versions of the survey were
       Paper: participants put answers in an envelope with ID#
        on it and return in person
       Computer: participants listened to questions in
        headphones and then answered on laptops.




                                                                  57
Types of Experimental Studies
   Completely Randomized Design
       The subjects are randomly assigned to one of the
        treatments.
   Matched Pairs Design
       Each subject is matched up with another subject who is
        similar in terms of age, health, etc.
         This creates a ______________ _______.

       The treatments are then randomly assigned to the subjects
        in each pair.
       This ensures that the treatment groups are essentially
        ______________.
                                                                58
Types of Experimental Studies
   Completely Randomized Design
       The subjects are randomly assigned to one of the
        treatments.
   Matched Pairs Design
       Each subject is matched up with another subject who is
        similar in terms of age, health, etc.
         This creates a matched pair.

       The treatments are then randomly assigned to the subjects
        in each pair.
       This ensures that the treatment groups are essentially
        ______________.
                                                                59
Types of Experimental Studies
   Completely Randomized Design
       The subjects are randomly assigned to one of the
        treatments.
   Matched Pairs Design
       Each subject is matched up with another subject who is
        similar in terms of age, health, etc.
         This creates a matched pair.

       The treatments are then randomly assigned to the subjects
        in each pair.
       This ensures that the treatment groups are essentially
        identical.
                                                                60
Types of Experimental Studies
   Crossover Design
       The subjects cross over during the experiment from one
        treatment to another.
   Randomized Block Design
       Similar subjects are matched up to create a large set of
        experimental units.
         This is called a _________.

       The treatments are then randomly assigned to units within
        the blocks.



                                                                    61
Types of Experimental Studies
   Crossover Design
       The subjects cross over during the experiment from one
        treatment to another.
   Randomized Block Design
       Similar subjects are matched up to create a large set of
        experimental units.
         This is called a block.

       The treatments are then randomly assigned to units within
        the blocks.



                                                                    62
Elements of a Good
Experiment
   Control group
       Allows us to compare against an existing treatment
       Enables us to control the __________ _______
           The placebo effect occurs when patients seem to improve
            regardless of the treatment they receive.
   Randomization
       Eliminates ______ that can result when researchers assign
        treatments to the subjects
       Balances the group on variables that you know affect the
        response
       Balances the group on _________ variables that may be
        unknown to you

                                                                      63
Elements of a Good
Experiment
   Control group
       Allows us to compare against an existing treatment
       Enables us to control the placebo effect
           The placebo effect occurs when patients seem to improve
            regardless of the treatment they receive.
   Randomization
       Eliminates ______ that can result when researchers assign
        treatments to the subjects
       Balances the group on variables that you know affect the
        response
       Balances the group on _________ variables that may be
        unknown to you

                                                                      64
Elements of a Good
Experiment
   Control group
       Allows us to compare against an existing treatment
       Enables us to control the placebo effect
           The placebo effect occurs when patients seem to improve
            regardless of the treatment they receive.
   Randomization
       Eliminates bias that can result when researchers assign
        treatments to the subjects
       Balances the group on variables that you know affect the
        response
       Balances the group on _________ variables that may be
        unknown to you

                                                                      65
Elements of a Good
Experiment
   Control group
       Allows us to compare against an existing treatment
       Enables us to control the placebo effect
           The placebo effect occurs when patients seem to improve
            regardless of the treatment they receive.
   Randomization
       Eliminates bias that can result when researchers assign
        treatments to the subjects
       Balances the group on variables that you know affect the
        response
       Balances the group on lurking variables that may be
        unknown to you

                                                                      66
Elements of a Good
Experiment
   Blinding
       Increases reliability of the results
           _________-blind: subjects do not know the
            treatment assignment
           _________-blind: neither the subjects nor those in
            contact with the subjects know the treatment
            assignment
   Replication
       Assigns several _________________ ________
        to each treatment

                                                                 67
Elements of a Good
Experiment
   Blinding
       Increases reliability of the results
           Single-blind: subjects do not know the treatment
            assignment
           _________-blind: neither the subjects nor those in
            contact with the subjects know the treatment
            assignment
   Replication
       Assigns several _________________ ________
        to each treatment

                                                                 68
Elements of a Good
Experiment
   Blinding
       Increases reliability of the results
           Single-blind: subjects do not know the treatment
            assignment
           Double-blind: neither the subjects nor those in
            contact with the subjects know the treatment
            assignment
   Replication
       Assigns several _________________ ________
        to each treatment

                                                               69
Elements of a Good
Experiment
   Blinding
       Increases reliability of the results
           Single-blind: subjects do not know the treatment
            assignment
           Double-blind: neither the subjects nor those in
            contact with the subjects know the treatment
            assignment
   Replication
       Assigns several experimental units to each
        treatment

                                                               70
Example 4.9
   A pharmaceutical company has developed a new drug for treating
    high blood pressure. To determine the effectiveness of the drug, the
    company conducted an experiment in which subjects with a history
    of high blood pressure were treated with the new drug.

   A later experiment randomly divided subjects with a history of high
    blood pressure into two groups. Group A was treated with the new
    drug as before. Group B received the most popular drug on the
    market at that time. The subjects were unaware of which treatment
    they received. 60% of the patients in Group A improved, while 63%
    of the patients in Group B improved.

   The __________ experiment is better because


                                                                      71
Example 4.9
   A pharmaceutical company has developed a new drug for treating
    high blood pressure. To determine the effectiveness of the drug, the
    company conducted an experiment in which subjects with a history
    of high blood pressure were treated with the new drug.

   A later experiment randomly divided subjects with a history of high
    blood pressure into two groups. Group A was treated with the new
    drug as before. Group B received the most popular drug on the
    market at that time. The subjects were unaware of which treatment
    they received. 60% of the patients in Group A improved, while 63%
    of the patients in Group B improved.

   The second experiment is better because it employs a control group
    and blinding.

                                                                      72
Example 4.10
   To investigate whether antidepressants help smokers to quit
    smoking, one study used 429 men and women who were 18 or older
    and had smoked 15 cigarettes or more per day in the previous year.
    They were all highly motivated to quit and in good health. They
    were assigned to one of two groups: one group took an
    antidepressant called Zyban, while the other group did not take
    anything. At the end of a year, the study observed whether each
    subject had successfully abstained from smoking.




                         Example taken from Statistics: The Art and   73
                              Science of Learning from Data
Logic Behind Randomized
Comparative Experiments
   Randomization ensures that the groups of subjects
    are similar in all respects before the treatments are
    applied.
   Using a control group for comparison ensures that
    external influences operate equally on both groups.
   If the groups are large enough, natural differences in
    subjects will average out.
   This means that there be little difference in the
    results for the groups unless the treatments
    themselves actually cause the difference.
                                                         74
Did You Know?
   Observational studies can also have control
    groups.
       These are called ______-________ studies.
       The cases are people who have a certain disease
        or condition, and the controls are people who do
        not have the disease.
       Their purpose is to see if one of the explanatory
        variables is related to the disease.
       _________ from the beginning of these notes is
        an example of a case-control study.

                                                        75
Did You Know?
   Observational studies can also have control
    groups.
       These are called case-control studies.
       The cases are people who have a certain disease
        or condition, and the controls are people who do
        not have the disease.
       Their purpose is to see if one of the explanatory
        variables is related to the disease.
       _________ from the beginning of these notes is
        an example of a case-control study.

                                                        76
Did You Know?
   Observational studies can also have control
    groups.
       These are called case-control studies.
       The cases are people who have a certain disease
        or condition, and the controls are people who do
        not have the disease.
       Their purpose is to see if one of the explanatory
        variables is related to the disease.
       Study 1 from the beginning of these notes is an
        example of a case-control study.

                                                        77
Important Points
   Observational studies
       Types
           Retrospective, Cross-Sectional, Prospective
       Sampling Designs
           Simple random sample (SRS), Stratified random sample,
            Cluster sample, Systematic sample
       Bias Types
           Undercoverage, Response bias, Nonresponse bias
       Sources of Bias
           Convenience sampling, Voluntary response sampling



                                                                    78
Important Points
   Experiments
       Types
           Completely randomized design, matched pairs designs,
            crossover designs, randomized block designs
       Elements of Good Experiments
           Control group, randomization, blinding and replication
       Advantages
           Can show causation
       Disadvantages
           Can be unethical
           Can take decades to complete
                                                                     79
Important Points
   If a group is underrepresented in the sample, we
    cannot make inference about it.
   We must be careful when interpreting the results of
    observational studies.
   For comparison of several treatments to be valid, you
    must apply all treatments to similar groups of
    experimental units.
   Interesting questions are usually pretty tough to
    answer. This is due in part to the fact that no single
    experiment or observational study can determine
    causation.

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