Motulsky, Prism 5 Statistics Guide, 2007, GraphPad Software Inc., San Diego CA, www.graphpad.com. To contact GraphPad Software, email [email protected] or [email protected] This Statistics Guide is a companion to GraphPad Prism 5. Available for both Mac and Windows, Prism makes it very easy to graph and analyze scientific data. How to order: Take a photo of your student ID with your phone and submit it to help.graphpad.com. If you don't have a student ID, you may send a letter from your advisor confirming your student status instead. We also accept faxed copies of your student verification (fax to US.858.454.4150). Be sure to include your email address on your fax.
GraphPad Prism for Windows; GraphPad Prism for Mac; Getting the Software. The latest version of GraphPad Prism can be downloaded free from the manufacturer via our Software Download service for use on Unmanaged devices only. Anyone using a University-Managed/Yoyo device must obtain all software via the Software Centre desktop shortcut. GraphPad Prism is a powerful combination of basic biostatistics, curve fitting and scientific graphing in one comprehensive program. Because it helps you organize, analyze and graph repeated experiments; pick appropriate statistical tests; and interpret the results, Prism is ideally suited to data plotting and analysis applications in the life.
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Common Statistical Tests in R and and Graphpad® Prism: Student's t-test
Synopsis
The student's t-test was first created by William Gosset, while doing analyses on beer in the Guiness Brewery in Dublin [citation needed]. Gosset used the pseudonym 'Student' on the paper that he published with the results, and the test that he developed became known as the 'Student's T-test', or simply a 't-test'.
There are a few different ways to run the t-test, but the methods to perform the test on each variant is similar in GraphPad Prism and R. Briefly, the tails for the t-test refer to how the data should be measured, meaning does the data go in one direction, or two. Nearly all biological experiments should use a two-tailed t-test. A paired t-test is typically used for datasets that are linked to each other in some way, such as treating cells at timepoint 1, then treating the same cells at timepoint 2, and testing whether the results are statistically significant. An unpaired t-test tests whether two unrelated datasets, such as if cells treated with Chemical X versus cells treated with Chemical Y have results that are statistically significantly different.
Additionally, a method using scipy with python has been added.
If you are uncertain about which test to use, typically a two-tailed, unpaired t-test is what you want.
The files for this are attached, as well as the Challenge Question Solutions.
Example Data
Unpaired t-test using Graphpad Prism
- Select 'Column', and the radio button for 'Enter replicate values, stacked into columns'
- Copy and paste the data, then select 'Analyze', and under Column analyses, select 't-tests (and non-parametric tests).
- Under Experimental Design, make sure the following is selected:
- Unpaired
- 'Yes, use parametric test'
- Unpaired t-test, assume both populations have the same SD.
Graphpad Prism Student Archives Version
Click OK. The results should look like this:
Unpaired t-test using R
import the table into R:
type the following command:
You should get the following output:
Comments: specifying 'var.equal = TRUE' forces R to run an equal variance t-test. In the Experimental Design step in Graphpad Prism, this would be the same as selecting 'Unpaired t-test, assume both populations have the same SD'. The default in R is
which runs a t-test and does not assume equal SD. This is the same in Graphpad Prism as selecting 'Unpaired t-test with Welch's correction. Do not assume equal SD'.
Unpaired t-test using python
Optional Challenge questions:
- Are the data normally distributed?
- How would you run a non-parametric test using Graphpad Prism and R?
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