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Advice for Chapter 9: Sampling Distributions

Know the difference between a population parameter, a sample statistic,
and the sampling distribution of a statistic

Know how to describe a sampling distribution…SOCS (usually no outliers)

Understand that sample statistics vary from sample to sample. However,
we are interested in the long-term pattern that develops from an infinite
numbers of samples.

The spread of a sampling distribution depends only on the sample size, not
the population size.

Understand the importance of bias and variability of a sample statistic.
The ideal situation is low bias and low variability.

Know the conditions under which a sampling distribution is approximately
Normal for both sample proportions and sample means

Understand how to apply the Central Limit Theorem.
o In general, a sample taken from a given population will have a
shape that models the shape of that population.
o So, if a population is Normally distributed, the sampling distribution
will also be Normally distributed.
o The Central Limit Theorem allows us to approximate the Normal
distribution for the sampling distribution of this statistic, for nonNormally distributed populations, as long as the sample size is large
(n> 30)

Don’t forget how to use Normal calculations to find probability.