Part II: Introduction to Hypothesis Testing
Download
1 / 24

Chapter 9 Two-Sample Tests - PowerPoint PPT Presentation


  • 121 Views
  • Uploaded on

Part II: Introduction to Hypothesis Testing. Chapter 9 Two-Sample Tests. Renee R. Ha, Ph.D. James C. Ha, Ph.D. Integrative Statistics for the Social & Behavioral Sciences. Paired t test. Also called Correlated Groups t test. Paired t test. Use when:

loader
I am the owner, or an agent authorized to act on behalf of the owner, of the copyrighted work described.
capcha
Download Presentation

PowerPoint Slideshow about ' Chapter 9 Two-Sample Tests' - miya


An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -
Presentation Transcript

Part II: Introduction to Hypothesis Testing

Chapter 9

Two-Sample Tests

Renee R. Ha, Ph.D.

James C. Ha, Ph.D

Integrative Statistics for the Social & Behavioral Sciences


Paired t test
Paired t test

  • Also called Correlated Groups t test


Paired t test1
Paired t test

  • Use when:

    • You have two measures on the same subjects (“before” and “after” measures are common).

    • You have two separate samples but the subjects in each are individually matched so that there are similar subjects in each group (but not the same subjects in each group).


Tables 9 1 9 2
Tables 9.1 & 9.2

  • Table 9.1: An example of “before” and “after” pairing using the same subjects in each “paired” sample

  • Table 9.2: An example of a “paired” design in which the actual subjects in each sample are different but are “matched” for characteristics which they have in common (in this example, genetics)




T test paired two sample for means
t-Test: Paired Two Sample for Means

  • Results if you use Microsoft Excel to calculate the t-test


Paired samples test
Paired Samples Test

  • Results if you use SPSS to calculate the t-test


t-Test: Paired Two Sample for Means

  • Results if you use Microsoft Excel to calculate the t-test


Paired Samples Test

  • Results if you use SPSS to calculate the t-test


When to use paired t test
When to use Paired t test

1. You have two samples and a within-groups design.

2. The sampling distribution is normally distributed.

3. The dependent variable is on an interval or ratio scale.


Independent t test
Independent t test

  • Two completely different (independent) groups of subjects that you want to compare to determine if they are significantly different from one another: a between-groups design.

  • E.g. Experimental and control conditions.

  • Compare the means of the two conditions/groups.


Independent t test1
Independent t test

  • Sampling Distribution of the Difference between sample means.

  • Determining variance when you have TWO estimates.




Independent t test formulas
Independent t test Formulas


T test two sample assuming equal variances
t-Test: Two-Sample Assuming Equal Variances

  • Results if you use Microsoft Excel to calculate the t-test


Independent samples test
Independent Samples Test

  • Results if you use SPSS to calculate the t-test


t-Test: Two-Sample Assuming Equal Variances

  • Results if you use Microsoft Excel to calculate the t-test


Independent Samples Test

  • Results if you use SPSS to calculate the t-test


When to use independent t test
When to use Independent t test

1. Two samples and a between-groups design.

2. The sampling distribution is normally distributed.

3. The dependent variable is on an interval or ratio scale

4. The variances of the two groups are the same or are homogeneous.


HOV

The homogeneity of variance assumption (HOV) requires that the variances of the underlying populations are equal or, in practical terms, not significantly different from one another.



ad