1. Distribution of the Sample Means
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2. Some Common Distribution Shapes
CHAPTER 6: NORMALLY DISTRIBUTED VARIABLES
(6.1)
Want to recall from our work in Ch. 2…some common distribution shapes.
At any given time in statistics, and in the world in general we can observe an enormous variety of variables. Many are different in the distribution that they form.
For example: the generation of random numbers would follow a uniform distribution.
Our exam scores and project scores thus far have followed a left skewed distribution.
Some variables though (actually many, especially naturally occurring ones) follow what is called a Normal Distribution.
You may be more familiar with the term “bell curve.”
This is a special distribution, and probably the most important distribution in statistics.
In statistics, we call variables whose distributions have this shape Normally Distributed Variables.
We call the distribution shape, a Normal Curve.

3. Distribution of the Sample Means
Sampling Error – the difference between the sample measure and the population measure due to the fact that a sample is not a perfect representation of the population.
Sampling Error – the error resulting from using a sample to estimate a population characteristic.
Recall: so far, we’ve been talking about variables (we’ve usually called x), and the distribution of these variables.
We’ve stated that the distribution of x has a mean we call mu, and a standard deviation we call sigma.
We’ve also stated that we can use a sample to acquire information about a population, and that this is most often preferable, since an entire census is often impossible.
This however, poses a problem, since the sample provides data for only a portion of the entire population. We cannot expect one sample to give us perfectly accurate information about the population of interest.
There is a certain amount of error that will result simply because we are sampling.
Hopefully, you will recall from earlier in the course, this type of error is called sampling error.
For Example: The Census Bureau publishes figures on the mean income of U.S. households. In 1993, the figure published was $41,428.
This figure is the sample mean (x-bar) income of the 60,000 households, NOT the population mean mu of all U.S. households, but we may ask ourselves, how accurate are such estimates likely to be? Is the estimate within $1,000, $5,000, etc.?
In order to answer this question, we would need to know the distribution of all possible sample means that could be obtained by sampling the incomes of 60,000 households. This distribution is called the distribution of the sample mean.
Let’s look at an example.

4. Distribution of the Sample Means
Distribution of the Sample Means – is a distribution obtained by using the means computed from random samples of a specific size taken from a population.
Distribution of the Sample Mean, – the distribution of all possible sample means for a variable x, and for a given sample size.
Recall: so far, we’ve been talking about variables (we’ve usually called x), and the distribution of these variables. In other words, how they vary about the mean.
In addition to knowing how individual data values vary about the mean for a population, we are sometimes also interested in knowing about the distribution of the means of samples taken from a population.
For example, Suppose a researcher selects 100 samples of a given size from a large population and computes the mean for each of the 100 samples. The values of these 100 means constitute a sampling distribution of sample means.
If the sample means are randomly selected, the sample means, for the most part, will be somewhat different from the population mean mu. These differences are caused by sampling error.

5. Properties of the Distribution of Sample Means
The mean of the sample means will be the same as the population mean.
The standard deviation of the sample means will be smaller than the standard deviation of the population, and it will be equal to the population standard deviation divided by the square root of the sample size.

6. An Example
Suppose I give an 8-point quiz to a small class of four students. The results of the quiz were 2, 6, 4, and 8.
We will assume that the four students constitute the population.

7. The Mean and Standard Deviation of the Population (the four scores)
The mean of the population is:
The standard deviation of the population is:

8. Distribution of Quiz Scores
A graph of the distribution of quiz scores.

9. All Possible Samples of Size 2 Taken With Replacement
SAMPLE MEAN SAMPLE MEAN
2, ,
2, ,
2, ,
2, ,
4, ,
4, ,
4, ,
4, ,
All possible samples of size 2 taken with replacement.

10. Frequency Distribution of the Sample Means
MEAN f
2 1
3 2
4 3
5 4
6 3
7 2
8 1
Shows the number of times each mean occurred.

11. Distribution of the Sample Means
Note the shape here.

12. The Mean of the Sample Means
Denoted
In our example:
So, , which in this case = 5

13. The Standard Deviation of the Sample Means
Denoted
In our example:
Which is the same as the population standard deviation divided by
Comment on sampling without replacement which is more the norm, and the Finite Population Correction Factor.

14. A Third Property of the Distribution of Sample Means
A third property of the distribution of the sample means concerns the shape of the distribution, and is explained by the Central Limit Theorem.

15. The Central Limit Theorem
As the sample size n increases, the shape of the distribution of the sample means taken from a population with mean and standard deviation
will approach a normal distribution. This distribution will have mean and standard deviation
We can use the Central Limit Theorem to answer questions about sample means in the same way that the normal distribution can be used to answer questions about individual values. The only difference is that a new formula must be used to obtain z-scores.

16. Two Important Things to Remember When Using The Central Limit Theorem
When the original variable is normally distributed, the distribution of the sample means will be normally distributed, for any sample size n.
When the distribution of the original variable departs from normality, a sample size of 30 or more is needed to use the normal distribution to approximate the distribution of the sample means. The larger the sample, the better the approximation will be.