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Chapter 7: Producing Data: Sampling

Knowledge Prerequisites

Variables

  1. declarative knowledge (definitions)
    1. distribution
  2. procedural knowledge
    1. how to construct a graph of data
  3. conditional knowledge
    1. how to describe a distribution
    2. how to compare two distributions
  4. resources
    1. Chapter 1 of text.
    2. Chapter 2 of text.

Learning Goals

  1. declarative knowledge (definitions)
    1. sample
    2. observational study
    3. experiment
    4. confounded variables
    5. biased study
    6. simple randam sample (SRS)
    7. probability sample
    8. stratified random sample
    9. undercoverage in surveys
    10. nonresponse in surveys
    11. response bias
  2. procedural knowledge
    1. how to construct a SRS
    2. how to construct a stratified random sample
  3. conditional knowledge
    1. how to identify response bias
    2. how to identify poorly worded questions
    3. how to identify 'valid' samples
    4. know 'key ideas' about sample design
    5. how to identify a sample from a polpulation
    6. know the advantages and disadvantages of the various sampling methods
    7. know when to use an appropriate sampling method
    8. know how to distinguish between an observational study and an experiment
    9. know what causes surveys to be problematic

Questions to Ponder

  1. What is the best way to study/prepare for the concepts in this section? Are there are other methods to study these concepts?
  2. What questions do you need to ask the instructor?

Purpose of this Section

Recall in Chapters 1 and 2, we analyzed data using graphs and numerical summaries. Now, we want to discuss how to collect data to answer our own questions.

General Notes

Samples are used instead of the population due to time restrictions, cost and convenience. We collect data on the sample and draw conclusions about the population.

Simple Random Sample

This is a sample chosen by chance. This type of sample is not biased because all individuals and all samples have an equal chance to be chosen. The selection of the sample includes using random numbers.

SRS is a building block of more elaborate sampling designs. But the analysis of more complex designs is beyond the scope of this course.

Random Number Generators
  1. Table B of text – we will NOT use this table in the course.
  2. Phone book!
  3. TI-83 calculator.
Surveys

NEVER trust results of surveys until you read the exact questions, know how the sample was determined and know the number of non-responses.

Inference about the population

Random samples eliminate some bias but it is unlikely that results of a sample are exactly the same as the entire population. The larger the sample, the more accurate the results.

Resources
  1. Simple Random Sample - http://bcs.whfreeman.com/ips4e/cat_010/applets/randomsample.html - This applet will construct a simple random sample of size n consisting of n individuals from the population chosen in such a way that every set of n individuals has an equal chance to be the sample actually selected.
  2. Simulating Senators Samples - http://statweb.calpoly.edu/chance/applets/senators/samplesenators.html - This applet draws simple random samples of size n of U.S. Senators and plots the sample statistics compared to the population statistics.
  3. Research Randomizer - http://www.randomizer.org/ - This web site is designed to assist researchers and students who want an easy way to perform random sampling or assign participants to experimental conditions.
  4. Two widely know polling organizations:
    1. Gallup Organization - http://www.gallup.com/ - The faq conatins information on how polls are conducted.
    2. Zogby International - http://zogby.com/ - The faq conatins information on how polls are conducted.

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