1. Part III
Gathering Data

2. Chapter 11 Understanding Randomness
An event is random if we know what outcomes could happen but not which particular values did or will happen
Random Numbers
“Hard to get”
Pseudorandom
Table of random digits
Pick a number from the next slide

4. Simulation
A simulation consist of a collection of things that happened at random. Is used to model real-world relative frequencies using random numbers.
Component
Situation that is repeated in the simulation. Each component has a set of possible outcomes
Outcome
An individual result of a simulated component of a simulation
Trial
The sequence of events that we are pretending will take place
Step-by-step page 295

5. Chapter 12 Sample Surveys
Idea 1: Examine a part of the whole
Carefully select a smaller group from the population (Sample)
A sample that does not represent the population in some important way is said to be biased

6. Sample Survey (cont.) Idea 2: Randomize
Randomizing protect us from the influences of all the features of our population, even the ones that we may not have thought about.
Is the best defense against bias, in which each individual is given a fair random chance of selection

7. Sample Surveys (cont.) Idea 3: It’s the sample size Census
The fraction of the population that you have sampled doesn’t matter. It’s the sample size itself that’s important.
Census
A Sample that consist of the entire population.
Difficult to complete. Not practical, too expensive
Populations are not static
Can be more complex

8. Populations and parameters
Population parameter
Parameter (numerical value) that is part of a model for a population. We want to estimate this parameters from sampled data.

9. Sampling
When selecting a sample we want it to be representative, that is that the statistics we compute from the sample reflect the corresponding parameters accurately
Simple Random Sample (SRS)
Is a sample in which each combination of elements has an equal chance of being selected
Sampling Frame
A list of individuals from which the sample is drawn

10. Other Sampling Designs
Stratified random sampling
A sampling design in which the population is divided into homogeneous subsets called strata, and random samples are drawn from each stratum.
Cluster Sampling
Random samples are drawn not directly from the population, but from groups of clusters. (Convenience, practicality, cost)

11. Other Sampling Designs (cont.)
Systematic Sample
Sample drawn by selecting individuals systematically from a sampling frame.
(ex. Every 10 people)
Multistage Sample
Combining different sampling methods

12. How to Sample Badly Sample badly with volunteers
Voluntary response bias invalidates a survey
Sample badly because of convenience
Convenience sampling: Simply include the individuals who are at hand
Sample from a bad sampling frame
Undercoverage
Some portion of the population is not sampled at all or has a smaller representation in the sample than it has in the population.

13. How to Sample Badly Non response bias Response Bias
Influence arising from the design of the survey wording.
Look for biases before the survey. There is no way to recover from a biased sample or a survey that asks biased questions
Sampling Variability
Difference from sample to sample, given that the samples are drawn at random

14. Exercises
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15. Chapter 13 Experiments Investigative Study Observational Studies
Researchers don’t assign choices
No manipulation of the factors
Retrospective study
Observational study in which the researcher identifies the subject and then collect data on their previous condition or behavior
Prospective Study
Identifies or selects the subjects and follows the future outcomes

16. Experiment Random assignment of subjects to treatments.
Explanatory Variable:
Factor (manipulate)
Response variable :
Measurement
Experimental units
Subjects
Participants
Factor
A variable whose levels are controlled by the experimenter
Levels of the factor
Treatments
All the combinations of the factors with their respective levels

17. The Four Principles of Experimental Design
1 - Control
We need to control sources of variation other than the factors being studied. (make the conditions similar for all treatment groups)
2 - Randomize
Assign the subjects randomly to the treatments to equalize the effects of unknown variation

18. The Four Principles of Experimental Design (cont.)
3 - Replicate
Apply the treatments to several subjects.
4 - Block
Separate in blocks of identifiable attributes that can affect the outcome of the experiment

19. Designing an Experiment
Step-by-Step Page 335

20. Experiments Control Treatment Blinding
Baseline treatment level to provide basis for comparison.
Blinding
There are two main classes of individuals who can affect the outcome of the experiment
Subjects, treatment administrators
Evaluators of the results
Single Blinding (one)
Double Blinding (both)

21. Experiments Placebos Blocking Confounding
A null treatment to make sure that the effect of the treatment is not due to the placebo effect.
Blocking
By blocking we isolate the variability due to the differences between the blocks so that we can see the differences due to the treatment more clearly
Confounding
When the levels of one factor are associated with the levels of another factor, we say that these two factors are confounded

22. Exercises
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