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The Language of Sampling

Learn about the importance of sampling, the variables used in statistical analysis, and different types of bias that can occur in sampling methods. Explore examples and understand how to minimize bias in statistical studies.

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The Language of Sampling

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  1. The Language of Sampling Lecture 5 Sections 2.1 – 2.4 Fri, Sep 8, 2006

  2. Why Sample? • Studying a sample gives us only partial information about a population. • So why not study (observe) the entire population? • Samples are random, so how can we expect a sample to be representative of the population?

  3. Why Sample? • We can prove mathematically that a large random sample has a very high probability of being representative of the population from which it is chosen.

  4. The Language of Sampling • Unit or subject. • Variable. • Population sizeN. • Sample sizen. • Parameter. • Statistic.

  5. Parameters and Statistics • For numerical data, we usually use the average of the values in the sample. • For example, the average household income. • What is the variable? • For non-numerical data, we usually use the proportion of observations in a specific category. • For example, the unemployment rate. • What is the variable?

  6. Random vs. Representative • Random sample. • Representative sample.

  7. Example • Study: Men Enjoy Watching Bad Guys Suffer • What were the populations? • What were the samples? • What were the variables? • What statistics were used? • What were the parameters?

  8. Bias • A sampling method is biased if it systematically produces a sample whose characteristics differ from those of the population. • Note that it is the method that is biased, not the sample.

  9. Two Biased Sampling Methods • Convenience sampling. • Volunteer sampling. • Examples?

  10. Three Types of Bias • Selection bias. • Nonresponse bias. • Response bias. • Experimenter bias. • Examples?

  11. Whose Fault is it? • Selection bias originates in the sampling procedure. • Nonresponse bias originates in the subjects who were selected for the sample, but chose not to participate. • Response bias originates in the subjects who are in the sample. • Experimenter bias originates in the experimenter.

  12. Examples • Phone surveys. • Use random-digit dialing. • Convenience sampling? • Volunteering sampling? • Selection bias? • Non-response bias? • Response bias? • Experimenter bias?

  13. Examples • Mailed surveys, including e-mail. • Mail individuals a survey and ask them to respond. • Convenience sampling? • Volunteering sampling? • Selection bias? • Non-response bias? • Response bias? • Experimenter bias?

  14. Examples • Internet survey. • Post the survey questions on the internet and let visitors respond at will. • Convenience sampling? • Volunteering sampling? • Selection bias? • Non-response bias? • Response bias? • Experimenter bias?

  15. Examples • Estimating average family size. • Randomly select individuals and ask them how many siblings they have. • Convenience sampling? • Volunteering sampling? • Selection bias? • Non-response bias? • Response bias? • Experimenter bias?

  16. Tomorrow • Bring your calculator!

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