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Transcript
AP Statistics
7.3B Notes
When the sample is large enough, the distribution of x is very close to Normal. This is true no matter what
shape the population distribution has, as long as the population has a finite standard deviation  . This
famous fact of probability theory is called the central limit theorem (sometimes abbreviated as CLT).
Central Limit Theorem
Draw an SRS of size n from any population with mean  and finite standard deviation  . The central limit
theorem (CLT) says that when n is large, the sampling distribution of the sample mean x is approximately
Normal.
How large a sample size n is needed for x to be close to Normal depends on the population distribution.
More observations are required if the shape of the population distribution is far from Normal. In that case,
the sampling distribution of x will also be very non-Normal if the sample size is small. Be sure you
understand what the CLT does – and does not – say.
Normal Condition for Sample Means
 If the population distribution is Normal, then so is the sampling distribution of x . This is true no
matter what the sample size n is.
 If the population distribution is not Normal, the central limit theorem tells us that the sampling
distribution of x will be approximately Normal in most cases if n  30.
The central limit theorem allows us to use Normal probability calculations to answer questions about sample
means from many observations even when the population distribution is not Normal.