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Module 4 - Coggle Diagram
Module 4
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Sampling Distributions
Recall that statistics will vary slightly from sample to sample, we call this sampling variability.
A sampling distribution is the collection of possible statistics we might obtain from samples of the same size from the same population.
When we randomly select a sample from a population, we are simultaneously randomly selecting its corresponding statistic from the sampling distribution.
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For example: Cat person?
Suppose that 45% (p = .45) of UGA students are "cat people." If I were to take a random sample of 50 UGA students, what is the probability that at least half of my sample (p hat >= .5) prefers cats?
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- Describe sampling distribution:
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- Find p(p^ >= .5), use normal calculator with mean = .45, SD = .070
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23.75% of sample of this type would yield a sample proportion showing that more than half of UGA students preferred cats.
General Properties
The mean of the sampling distribution is always the same as the parameter, regardless of the sample size. It is assumed the sampling is unbiased.
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The sampling distribution has oftentimes bell-shaped, but this seemed to depend on part on the sample size being large enough.
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