Other's OnLine Resources (Chapter 10)
Comparing
2 Correlated Means With a tTest 
 Description:
In using this interactive online resource, you'll be able to
see how a ttest functions to compare the means of 2 correlated
samples. You'll also see a wonderful pictorial representation
of the "matching" (or what some would refer to as "pairing") within
the data.
 What to Do:
 Click on the colored title of this online resource: "Comparing
2 Correlated Means With a tTest."
 After clicking on "Begin," click "Simulate" in the next
screen you see.
 Look inside the gray box and find the 2 sample means, the
ttest's df, the calculated value, and the critical value.
(Is df+1 = the number of pairs of scores?)
 Look in the upper righthand corner to see whether your
ttest comparison produced a statistically significant result.
 Repeat Steps 24 several times. Then click "Simulate 5000."
(Do approximately twothirds of your replications turn out
significant?)
 Sky Huck's Puzzle Question:
If you click "Reset" and then change "n" from 8 to 100, how likely
is it that the ttest will yield a significant difference between
the two sample means? After making your guess, click "Simulate
5000" to see.

Comparing
2 Independent Means With a tTest 
 Description:
In using this interactive online resource, you'll be able to
see how a ttest functions to compare the means of 2 independent
samples. You'll also see the absence of any "matching" (or what
some would refer to as "pairing") within the data.
 What to Do:
 Click on the colored title of this online resource: "Comparing
2 Independent Means With a tTest."
 After clicking on "Begin," change (in the next screen) the
number for "Population rho" from 0.50 to 0.00. Then click
"Simulate."
 Look inside the gray box and find the 2 sample means, the
ttest's df, the calculated value, and the critical value.
(Is df+2 = the total number of scores in both samples combined?)
 Look in the upper righthand corner to see whether your
ttest comparison produced a statistically significant result.
 Repeat Steps 24 several times. Then click "Simulate 5000."
 Sky Huck's Puzzle Question:
If you click "Reset" and then change "n" from 8 to 100, how likely
is it that the ttest will yield a significant difference between
the two sample means? After making your guess, click "Simulate
5000" to see.

Estimation
With a Mean 
 Description:
This interactive online resource allows you to build a 95% confidence
interval around a sample mean, with you being in control of the
numerical values of the sample's M, SD, and n.
 What to Do:
 Click on the colored title of this online resource: "Estimation
With a Mean."
 In the 3 boxes, set the sample mean = 100, the standard
deviation = 15, and n = 25.
 Click on the gray bar that says "Calculate the Confidence
Interval."
 On the next screen that pops up, look at the CI that's been
built for your situation.
 Click on your browser's "Back" button, and then repeat Steps
24, varying the values of M, SD, and n.
 Sky Huck's Puzzle Question:
With M, SD, and n initially set equal to 100, 15, and 5, respectively,
how much do you need to increase n (while holding M and SD constant)
in order to cut the CI in half?

Regression
Toward the Mean 
 Description:
You'll sometimes be confronted with a "research discovery" that
comes from a study in which a single group of people was first
pretested, then given some form of treatment, and finally posttested,
with a correlated ttest used to compare the group's pretest and
posttest means. This clearlywritten online resource, with its
wonderful graphics, explains why such a ttest comparison can,
in certain circumstances, fail to assess correctly the treatment's
worth.
 What to Do:
 Click on the colored title of this online resource: "Regression
Toward the Mean."
 Carefully read the text material and examine the pictures.
 Sky Huck's Puzzle Question:
Suppose a group of 200 of people is tested, yielding a mean of
80 and a SD of 8. Next suppose that those folks (n = 10) who score
the worst (M =60, SD = 2) are identified, given some special training
for 2 weeks to improve their skills, and then administered a posttest
after the training. (A correlated ttest is used to compare the
small group's pretest and posttest means to see whether the training
worked.) Finally, assume that (1) the test used to in this study
is known to have a testretest reliability of .60 when used with
folks like those who went through the training, and (2) that people
generally improve their test performance by 5 points when retested,
simply because they understand the format better, are familiar
with the time limit, etc. Knowing all this, how well do you think
the subgroup that went through training would do on the posttest,
on average, EVEN IF THE TRAINING PROGRAM HAD ABSOLUTELY NO BENEFIT
WHATSOEVER?

