I am finishing up with a class and the question came up that they did not understand why we do not recommend a probability plot for attribute yield data. The students really like to see the variability as the 80% range. They also want to tell their leadership to expect the yield to fall in that 80% range.
Their motivation is good. You should be able to give a range of values for yield that indicates a change, but the probability plot is not the right tool. We use the probability plot with continuous data because the process mean and the process variation (standard deviation) are independent. A process change is able to change the mean and not change the variability. This can not occur with a yield or failure percentage.
Binomial (pass-fail) data is limited by a percentage to be between 0 and 1. This distribution has only a single parameter p, the defect rate, which is equivalent to the average performance. The standard deviation is a function of the same p and the sample size, actually it is the square root of (p*(1-p))/sqrt(n).
In a simple minitab demo (make two columns of data with binomial random data with a defect p of 0.10. One has an n=100, one has a n=1000. covert each to a percentage and then compare the probability plots. Both have the same defect rate but the standard deviation is quite different.
This is the result. Note the same 50% point but different slopes and standard deviations. What this means is that the probability plot will show a different range of results based on the sample size. It is not easy to explain. That is why we stick to just an i-chart
If you really need to share a range to expect, use the i-chart control limits. Because if it goes outside of them, it is a special cause indication. This is better than using a probability plot
Good luck