Outline: T-Tests: Lecture 7 (01:45)
In this lecture, Prof. Naomi Lowe explains T-tests including one-sample, dependent/paired, standard deviation and variance, degrees of freedom, and T distribution.
Intro to Student's T: SD/Var of a Sample (07:35)
William Gosset published articles under the alias "Student" while working for Guinness. Use Student's T when the population variance is unknown. Standard deviation is the square root of variance; see an unbiased estimate example.
Intro to Student's T: Degrees of Freedom (DF) (04:59)
DF is the number of scores that are free to vary. Learn the formula for calculating variance using degrees of freedom and see examples.
Intro to Student's T: Variance of Means (04:02)
This formula determines the spread of all possible sample means. See an example of the standard deviation of the distribution of means.
Intro to Student's T: T Distribution (05:22)
The distribution of all possible sample means depends on the number in the sample. The T-curve extends with smaller degrees of freedom on the graph.
One-Sample T (12:44)
The T-test works with a sample from one population. This example determines the knowledge of individuals who have seen paid political ads using a T formula that is similar to the Z formula.
Dependent/Paired T (12:57)
The T-test uses one sample two times or paired samples. Use the difference scores, assume the population mean difference is null, and use the same T-distribution to make the comparison. See examples of a dependent T-test and a paired T-test.
Power & Effect Size (06:06)
Insert the effect size into a power table to determine possible powers. See an effect size example for one-sample T-tests and dependent T-tests.
For additional digital leasing and purchase options contact a media consultant at 800-257-5126
(press option 3) or email@example.com.