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Chong Ho (Alex) Yu
One-sample z-test and one-sample t-test
Test the sample mean against the population
mean
To see whether there is a big gap between the
sample and the population
To see whether the sample comes from or belongs
to the population.
Seldom used. Why?
Enter the population
mean and SD to do the
z-test
Enter the mean only
to do the t-test.
But if you already
know about the
population, then you
don’t need statistics.
Usually you don’t
know!
You need two independent groups e.g. boys and
girls.
Test whether there is a performance gap
between boys and girls
T-test is a test to get the t-ratio.
The difference between two means based on
the standard deviation.
Virtually any comparison test is about looking at
the difference adjusted by a common standard
Otherwise, it will be comparing apples and
oranges!
Spot outliers using the boxplot
What are these?
Standard error
Upper and Lower CL
P value
In experiments we want to have two
comparable groups; we want to reduce bias.
We will divide the class into two groups
But the two groups must be equivalent or
symmetrical
i.e. the same numbers of two genders; the same
numbers of different races; the same numbers
of different SES, religion…etc.
Can you do that?
Also known as 2 correlated sample t-test
Paired t-test
When you have no control group…
You are your own control. The person that is
most similar to you is: YOURSELF!
Spot ceiling or floor effects
Download the data set “between within” from
Chapter 14 folder.
Run a 2-sample independent t-test.
Spot and exclude outliers, if there is any.
Use the midterm as the DV
Use sex (gender) as the IV
Report the confidence intervals, the t-ratio and
the p value
Is there any performance gap between boys and
girls?
Use the same data set
Run a paired t-test
Use test and midterm as the variables
Spot and exclude students that show floor or
ceiling effects, if there is any.
Report the confidence intervals, the t-ratio and
the p value
Is there any significant change/growth between
the pretest and the midterm?