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Sampling Confounding Variables
STAT 113 Sampling, Randomization and Confounding
Colin Reimer Dawson
Oberlin College
Sampling Confounding Variables
STAT 113 Sampling, Randomization and Confounding Colin Reimer - - PowerPoint PPT Presentation
STAT 113 Sampling, Randomization and Confounding Colin Reimer - - PowerPoint PPT Presentation
Sampling Confounding Variables STAT 113 Sampling, Randomization and Confounding Colin Reimer Dawson Oberlin College September 6, 2019 1 / 12 Sampling Confounding Variables Sampling and Inference: The Big Picture 3 / 12 Sampling
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Sampling Confounding Variables
Population, Samples, and Inference
Population: All potential cases that we are interested in saying something about Sample: The set of cases we actually have data for (a subset of the population) Statistical Inference: Using sample data to
- btain information about the population
For inference to be effective, samples ought to be representative of the population.
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Sampling Confounding Variables
Simple Random Sampling
- To guard against sampling bias, we typically want to
collect a random sample. Versions...
- Simple Random Sampling
- Stratified Sampling
- Cluster Sampling
- Systematic Sampling
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Sampling Confounding Variables
Feasibility of Random Sampling
It is often not feasible to get a truly random sample. Options:
- Sample from a subset of the population; generalize
conservatively
- Collect a non-random sample, avoid bias related to
variables of interest 6 / 12
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Sampling Confounding Variables
Not all Non-Random Samples are Created Equal
You want to estimate the average hours per week that Oberlin students spend studying. None of the following is random; which would you go with? (a) Go to Mudd and use a RNG to select people to ask (b) Email every student and use all responses (c) Require all students in a random statistics class to respond (d) Go to the gym and ask everyone going in (e) Stand outside The Local and ask every fifth person entering 7 / 12
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Sampling Confounding Variables
Not all Non-Random Samples are Created Equal
None of the above are representative in every way, but some are more obviously non-representative for the variable we care about.
Sampling Bias is a problem when the chance a case has of being selected is associated with one or more of the variables being collected.
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Sampling Confounding Variables
“Don’t Ask Don’t Tell”
2010 CBS/NYT polls (when DADT was being reconsidered): “Do you favor or oppose homosexuals gay men and lesbians serving
- penly in the military?”
Favor Oppose “homosexuals” 44% 42% “gay men and lesbians” 58% 28%
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Sampling Confounding Variables
Non-Sampling Bias
Other sources of bias not due to sampling procedure
- Question wording
- Non-response bias
- Context
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Sampling Confounding Variables