1. Where we’ve been and where we’re going…
Chapters 1 & 2
Exploratory Data Analysis
Exploration. Formulating questions and hypotheses.

2. Where we’ve been and where we’re going…
Chapters 1 & 2
Chapter 3
Exploratory Data Analysis
Producing Data
Exploration. Formulating questions and hypotheses.
Acquiring knowledge and information to address the questions.

3. Where we’ve been and where we’re going…
Chapters 1 & 2
Chapter 3
Chapter 4 & Beyond
Exploratory Data Analysis
Producing Data
Statistical Inference
Exploration. Formulating questions and hypotheses.
Acquiring knowledge and information to address the questions.
Answering the questions (with numerical methods).

4. Collecting Data
Arrangements for collecting data from many individuals are called designs.
Important Design Questions
How many individuals will be observed?
How are individuals selected?
How will groups be formed among the selected individuals, if pertinent to the study?

5. Anecdotal Evidence
We tend to rely on data that most easily comes to mind.
Unusual events or individuals
Generalizations
Anecdotal evidence is composed of haphazardly selected cases.
May not be representative of the whole.
Do not trust it!

6. Available Data
Sometimes it’s convenient to find good data sets that have already been collected and use it to answer our question(s).
Advantages
Less work for us!
Many sources of available data exist.
Limitations
Data wasn’t collected specifically for our purpose.
How was it collected? Biases? All lurking variables accounted for?

7. Sample vs. Census
Census: an attempt to observe every individual in a population.
A census is expensive and time-consuming.
Sample: observation of a selected number of individuals from a population.
Easier to implement than a census.
Must take care in how the sample is chosen!

8. Observational Studies
A sampling of the population is a type of observational study.
Individuals are observed, but not controlled.
No matter how carefully chosen, confounding variables might exist.
Cause-and-effect relationships cannot be established on the basis of observational studies.

9. Controlled Experiments
We manipulate the levels of one or more variables for an individual.
Explanatory variables are called factors.
Treatment is the manipulation of explanatory variables.
Specific values of explanatory variables are called levels.

10. Controlled Experiments
Treatment group(s): Groups of individuals that will experience treatment (changes in the factors of interest).
Control group: Group of individuals who will not receive any treatment.
Why is a control group important?
How should we assign individuals to groups?

11. Grouping Subjects
It’s hard to match treatment and control groups with respect to confounding factors, though it is sometimes attempted.
It’s often best to randomize subjects to treatment or control groups.
Removes potential biases. (A study is biased if it systematically favors certain outcomes).

12. Example
In one early trial of coronary bypass surgery a physician performed the surgery on a test group, 98% of whom survived at least 3 years.
Previous studies showed that 68% survived at least 3 years with conventional treatment.
A newspaper commented on the physician’s results as “spectacular”. What do you conclude?