You generally use the Analyze > Specialized Modeling > Matched Pairs for this purpose, but this platform does not currently provide an equivalence test. For now, use a formula to compute the difference in the data table. I mocked up paired responses Y1 and Y2.
![table.PNG table.PNG](https://community.jmp.com/t5/image/serverpage/image-id/48034i53371EB16319F172/image-size/large?v=v2&px=999)
Select Analyze > Distribution and launch it with Difference in the Y role. Click the red triangle next to Difference and select Test Equivalence:
![menu.PNG menu.PNG](https://community.jmp.com/t5/image/serverpage/image-id/48035iF6BCBDECFF8F89D4/image-size/large?v=v2&px=999)
Specify 0 for the expected mean and whatever limit you use for practical equivalence. I claim that my example is equivalent if the result is within 1 unit:
![dialog.PNG dialog.PNG](https://community.jmp.com/t5/image/serverpage/image-id/48036i0EB05E504D3D290D/image-size/large?v=v2&px=999)
Click OK, and you will get the result:
![result.PNG result.PNG](https://community.jmp.com/t5/image/serverpage/image-id/48037i9475F5D2CE9C5152/image-size/large?v=v2&px=999)
This example shows that these two responses are practically equivalent because the two-sided tests are significant and the confidence interval estimate of the mean is entirely within the +/- 1 interval specified.