Has anyone on this forum actually done an analysis of sensitivity of CI to changes in the amounts of developing agents? Use the "Magic Square" type of experimental plan to get a "ring around" with minimum number of tests.
In case anyone is interested in what the "Magic Square" that Pat mentions here, it's a really interesting way to design tests. It took me a day to remember that actual name of the test - it's properly called an "orthogonal array test", in the past, a "Latin Square" test, and sometimes called a "Taguchi test" after the person, 田口 玄- Taguchi Genichi, who developed and popularised the method.
It allows one to design a test, say of "X" number of variables, by only performing X+1 tests. For each variable, a high level and low level are chosen. So for say hydroquinone amount in a developer, one amount of hydroquinone is used, and then either a higher or lower amount is used in comparison.
So if you had a developer with 3 things, say 1 g. metol, 1 g. hydroquinone, and 100 g. sodium sulfite, you would then pick another amount for each amount:
Parameter 1 (metol) - Level 1 = 1 g, and Level 2 = 2g
Parameter 2 (hydroquinone) - Level 1 = 1 g, and Level 2 = 2g.
Parameter 3 (sodium sulfite) - Level 1 = 100 g, and Level 2 = 50 g.
You then make an array of these amounts:
You can see how each parameter is varied in each horizontal row. None of the 4 rows are the same. So after you do the test, you have some quantifiable measurement you can do (measure CI, measure accutance, measure resolution) and then you take the results of the measurment on each test, and then you do a few simple statistics (that I don't have the time to adequately describe here) and you can find out which changes in each parameter had the most or least affect on the outcome of the tests.
It's a really powerful system of designing tests and well worth it if you are going to fully explore some of the issues that can arise when testing things. And the cool thing is that it can be applied to any system where you can make changes in parameters and measure the changes.