1. The standard error of the sample mean and confidence intervals
How far is the average sample mean from the population mean?
In what interval around mu can we expect to find 95% or 99% or sample means

2. An introduction to random samples
When we speak about samples in statistics, we are talking about random samples.
Random samples are samples that are obtained in line with very specific rules.
If those rules are followed, the sample will be representative of the population from which it is drawn.

3. Random samples: Some principles
In a random sample, each and every score must have an equal chance of being chosen each time you add a score to the sample.
Thus, the same score can be selected more than once, simply by chance. (This is called sampling with replacement.)
The number of scores in a sample is called “n.”
(Small n, not capital N.)
Sample statistics based on random samples provide least squared, unbiased estimates of their population parameters.

4. The first way a random sample is representative of its population
One way a random sample will be representative of the population is that the sample mean will be a good estimate of the population mean.
Sample means are better estimates of mu than are individual scores.
Thus, on the average, sample means are closer to mu than are individual scores.

5. The variance and the standard deviation are the basis for the rest of this chapter.
In Chapter 1 you learned to compute the average squared distance of individual scores from mu. We called it the variance.
Taking a square root, you got the standard deviation.
Now we are going to ask a slightly different question and transform the variance and standard deviation in another way.

6. As you add scores to a random sample
Each randomly selected score tends to correct the sample mean back toward mu
If we have several samples drawn from a single population, as we add scores to each sample, each sample mean gets closer to mu.
Since the sample means are all getting closer to mu, they will also be getting closer to each other.

7. As you add scores to a random sample – larger vs. smaller samples
The larger the random samples, the closer their means will be to mu, on the average.
Therefore: The larger the random samples, the closer their means will be to each other, on the average.

8. Let’s see how that happens
Population is 1320 students taking a test.
 is 72.00,  = 12
Let’s randomly sample one student at a time and see what happens.We’ll create a random sample with 8 students’ scores in the sample.

9. Test Scores F Scores r e q u Mean n c y score Sample scores: Means:
Standard
deviations
score 72 84
Sample scores:
102 72 66 76 78 69 63
Means: 87 80 79
76.4
76.7
75.6
74.0

10. How much closer to mu does the sample mean get when you increase n, the size of the sample? (1)
The average squared distance of individual scores from mu, the population mean is called the variance. You learned to compute it in Chapter 1.
The symbol for the mean of a sample is the letter X with a bar over it.We will write that as X-bar or .

11. Let’s put that in a formula sigma2X-bar = sigma2/n
How much closer to mu does the sample mean get when you increase n, the size of the sample? (2)
The average squared distance of sample means from mu is the average squared distance of individual scores from mu divided by n, the size of the sample.
Let’s put that in a formula
sigma2X-bar = sigma2/n

12. Then take a square root to obtain the standard error of the mean, the average unsquared distance of sample means from mu.
This is important. So here are several definitions of the standard error of the mean. They are all correct, but ignore any that confuse you!

13. The standard error of the sample mean
As you know, the square root of the variance is called the standard deviation. It is the average unsquared distance of individual scores from mu.
The average unsquared distance of sample means from mu is the square root of sigma2X-bar
The square root of sigma2X-bar = sigmaX-bar.
sigmaX-bar is called the standard error of the sample mean or, more briefly, the standard error of the mean. Here are the formulae
sigma2X-bar = sigma2/n
(Then, to get the standard error of the mean take a square root of sigma2/n).
sigmaX-bar = sigma/

14. The standard error of the mean
Let’s translate the formula into English, just to be sure you understand it. Here is the formula again: sigmaX-bar = sigma/
In English: The average (unsquared) distance of sample means from mu equals the ordinary standard deviation divided by the square root of the sample size.

15. Still another way to say the same thing: The standard error of the mean is the standard deviation of the means of random samples of a specific size (n). This last definition sometimes confuses people. If it confuses you, just remember: The standard error of the mean is the averaged unsquared distance of sample means from mu.

16. Let’s check and make sure that the formula is correct
Let’s check and make sure that the formula is correct. Let’s see that the standard error equals the ordinary standard deviation divided by the square root of n. To do that, let’s start with a tiny population: N=5
Here are all the scores in a population: 1,3,5,7,9.
The scores in this population form a perfectly rectangular distribution.
Mu = 5.00

18. Computing sigma sigma = 2.83
SS=(1-5)2+(3-5)2+(5-5)2+ (7-5)2+ (9-5)2=40
sigma2=SS/N=40/5=8.00
sigma = 2.83

19. If we did compute a standard deviation of sample means from mu, it should give the same result as the formula
Let’s see if it does.
We can only do all the computations if we have a very small population and an even tinier sample.
Let’s use the population of 5 scores we just looked at (sigma = 2.83). We’ll look at all the samples with n = 2.
If the formula is right, the average unsquared distance of sample means, n=2, should be 2.83/ = 2.83/1.414 = 2.00.
Is that right? To find out, let’s compute the standard error of the mean from the differences between the means of all possible samples (n=2) from mu.

23. The standard error = the standard deviation divided by the square root of n, the sample size
The formula works. And it works every time.

24. Let’s see what sigmaX-bar can tell us
We know that the mean of SAT/GRE scores = 500 and sigma = 100
So 68.26% of individuals will score between 400 and 600 and 95.44% will score between 300 and 700
REMEMBER THAT SAMPLE MEANS FALL CLOSER TO MU, ON THE AVERAGE, THAN DO INDIVIDUAL SCORES.

25. What happens when we take random samples of SAT scores with n=4?
The standard error of the mean is sigma divided by the square root of the sample size = 100/2=50.
SAMPLE MEANS FALL INTO A NORMAL CURVE EVEN BETTER THAN INDIVIDUAL SCORES
68.26% of the sample means (n=4) will be within 1.00 standard error of the mean from mu and 95.44% will be within 2.00 standard errors of the mean from mu
So, 68.26% of the sample means (n=4) will be between 450 and 550 and 95.44% will fall between 400 and 600

26. Let’s make the samples larger
Take random samples of SAT scores, with 400 people in each sample, the standard error of the mean is sigma divided by the square root of 400 = 100/20=5.00
68.26% of the sample means will be within 1.00 standard error of the mean from mu and 95.44% will be within 2.00 standard errors of the mean from mu.
So, 68.26% of the sample means (n=400) will be between 495 and 505 and 95.44% will fall between 490 and 510.
See how sample means get closer and closer to mu as sample size increases!

27. The Central Limit Theorem

28. What happens as n increases?
The sample means get closer to each other and to mu.
Their average squared distance from mu equals the variance divided by the size of the sample.
Therefore, their average unsquared distance from mu (which is called the standard error of the mean) equals the standard deviation divided by the square root of the size of the sample.
The sample means fall into a more and more perfect normal curve.
These facts are called “The Central Limit Theorem” and can be proven mathematically.

29. CONFIDENCE INTERVALS

30. We want to define two intervals around mu: One interval into which 95% of the sample means will fall. Another interval into which 99% of the sample means will fall.

31. 95% of sample means will fall in a symmetrical interval around mu that goes from standard errors below mu to standard errors above mu
A way to write that fact in statistical language is:
CI.95: mu sigmaX-bar or
CI.95: mu sigmaX-bar < X-bar < mu sigmaX-bar

32. As I said, 95% of sample means will fall in a symmetrical interval around mu that goes from standard errors below mu to standard errors above mu
Take samples of SAT/GRE scores (n=400)
Standard error of the mean is sigma divided by the square root of n=100/ = 100/20.00=5.00
1.960 standard errors of the mean with such samples = (5.00)= 9.80
So 95% of the sample means with n=400 can be expected to fall in the interval
= and =509.80
CI.95: mu sigmaX-bar = or
CI.95: < X-bar <

33. 99% of sample means will fall within 2.576 standard errors from mu
Take the same samples of SAT/GRE scores (n=400)
The standard error of the mean is sigma divided by the square root of n=100/20.00=5.00
2.576 standard errors of the mean with such samples =
2.576 (5.00)= 12.88
So 99% of the sample means can be expected to fall in the interval
= and =512.88
CI.99: mu sigmaX-bar = or
CI.99: < the sample mean <

34. Notice that the 99% CI includes the 95% CI. With n=400, mu=500
Notice that the 99% CI includes the 95% CI. With n=400, mu=500.00, sigma=100.00
CI.95: < X-bar <
CI.99: < X-bar <

35. You do one Using SAT scores, with n=2500:
Into what interval should 95% of the sample means fall?

36. Interval around mu
95% of the sample means should fall within standard errors of the mean from mu.
Given that sigmaX-bar =2.00, you multiply * sigmaX-bar = x 2.00 = 3.92

37. Thus:
95% of the sample means should fall in an interval that goes 3.92 points in both directions around mu
500 – 3.92 = =
So 95% of sample means (n=2500) should fall between and
CI.95: < X-bar <