Use a 2 proportions test to analyze observed differences in the process proportion (defective) at two settings of an input. Use this test when the data from the process are discrete and have exactly two levels, for example, pass or fail, and the factor being evaluated has exactly two levels, for example, fast or slow, before or after. You must collect a sample at each level of the input variable.
When to Use | Purpose |
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Mid-project | Fixing an input at two different settings (levels) helps to determine which inputs have significant influence on the proportion defective of the output. |
End of project | Verify a significant reduction in the process proportion defective results from the implemented improvements. Of course, this step assumes one of the goals of the project was to reduce the proportion defective. |
Your data must be discrete Y values at two levels (for example, good or bad) and a single X at two levels.
For more information, go to Insert an analysis capture tool.
Use a 2-sample t-test to analyze the difference between the observed process mean at two settings of an input. To use a 2-sample t-test, you must collect a sample of data at both levels of the input variable.
When to Use | Purpose |
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Mid-project | Fixing an input at two different settings (levels) helps determine which inputs have significant influence on the mean of the output. |
End of project | Verify a significant difference exists between the means of the pre-project process and the post-project improved process. Of course, this assumes that one of the goals of the project was to shift the location of the process (change the process mean). |
Your data must be values for continuous Y (output) and a single X (input) at two levels.
For more information, go to Insert an analysis capture tool.
Use the Mann-Whitney test to analyze the observed differences in the process median between two input settings. This test is similar to a 2-sample t-test, and is an alternative for cases where the data from the two samples are not reasonably normal.
When to Use | Purpose |
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Mid-project | Test which inputs have significant influence on the output by fixing an input at two different settings (levels). |
End of project | Verify a significant difference exists between the medians of the pre-project process and the post-project, improved process. Of course, this step assumes one of the goals of the project was to shift the process median. |
Your data must be a continuous value for Y (output), and a single X (input) at two levels.
For more information, go to Insert an analysis capture tool.
Use a paired t-test to analyze observed differences in sample units that are subjected to two different inputs. To use a paired t-test, you must subject the exact same sample units to both levels of the input variable to remove potential effects due to differences in the sample units themselves, which might mask the effect due to the change in the input. For example, you want to test whether two types of tires result in differences in gas mileage. The variation in gas mileage due to different cars in the sample would be much greater than variation due to different tires. A paired t-test accounts for the differences in cars.
When to Use | Purpose |
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Mid-project | Fixing an input at two different settings (levels) helps to determine which inputs have significant influence on mean of the output. |
Mid-project | Verify changes to inputs result in significant differences from the pre-project mean, provided you can test on the same units as those in the pre-project sample. |
Your data must be values for continuous Y (output) and one X (input) at two levels.
For more information, go to Insert an analysis capture tool.