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## Standard deviation and p-value for difference scores?

Does anyone know how to create standard deviations and p-values if you are looking at the difference scores of two averages? I think I have to do a one tailed t-test but I am really unsure how I would do that in JMP.

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Staff

## Re: Standard deviation and p-value for difference scores?

Hi syy010,

It sounds like you're looking to perform a matched-pairs/dependent-measures t-test. When you have data in dependent pairs (e.g. repeated measurements from the same individual, or across matched individuals) the analysis boils down to a one-sample t-test on the difference scores (*this is a different issue than a "one-tailed test" -- see below). These can be performed in JMP using the Analyze > Matched Pairs platform, or by using Analyze > Distribution after making a difference score column. In the latter case, you are testing whether the mean of the difference score column is different from 0 (which can be done by selecting the Red Triangle in the distribution platform > Test Mean). You will also get the standard deviation of the difference scores by using this second approach.

Here is a page with details on these kinds of tests, including a video and a PDF with step-by-step instructions:

https://community.jmp.com/docs/DOC-6775

You can find a complete listing of these JMP guides here: Collection: All One-Page Guides

I hope this helps!

julian

*A "one-tailed" test is a separate issue, and that has to do with how you specify your null and alternative hypotheses. A one-tailed hypothesis test, or a directional test, in when you are testing an alternative hypothesis in one direction (e.g. whether there is specifically an increase, or specifically a decease) rather than whether there is evidence of any difference (direction ignored). Directional hypotheses (one-tailed tests) are carried out the same way in JMP, just look at the p > t or p < t sections (depending on the direction you specify) rather than the p > |t| sections. If you'd like to know more about directional hypothesis (and some considerations when using them) here is a video of mine covering them in more detail:        Factors Affecting Power - Directional Hypotheses (Module 1 8 9)

Staff

## Re: Standard deviation and p-value for difference scores?

Hi syy010,

It sounds like you're looking to perform a matched-pairs/dependent-measures t-test. When you have data in dependent pairs (e.g. repeated measurements from the same individual, or across matched individuals) the analysis boils down to a one-sample t-test on the difference scores (*this is a different issue than a "one-tailed test" -- see below). These can be performed in JMP using the Analyze > Matched Pairs platform, or by using Analyze > Distribution after making a difference score column. In the latter case, you are testing whether the mean of the difference score column is different from 0 (which can be done by selecting the Red Triangle in the distribution platform > Test Mean). You will also get the standard deviation of the difference scores by using this second approach.

Here is a page with details on these kinds of tests, including a video and a PDF with step-by-step instructions:

https://community.jmp.com/docs/DOC-6775

You can find a complete listing of these JMP guides here: Collection: All One-Page Guides

I hope this helps!

julian

*A "one-tailed" test is a separate issue, and that has to do with how you specify your null and alternative hypotheses. A one-tailed hypothesis test, or a directional test, in when you are testing an alternative hypothesis in one direction (e.g. whether there is specifically an increase, or specifically a decease) rather than whether there is evidence of any difference (direction ignored). Directional hypotheses (one-tailed tests) are carried out the same way in JMP, just look at the p > t or p < t sections (depending on the direction you specify) rather than the p > |t| sections. If you'd like to know more about directional hypothesis (and some considerations when using them) here is a video of mine covering them in more detail:        Factors Affecting Power - Directional Hypotheses (Module 1 8 9)