> If you make a conclusion on made up data you get bogus conclusion
No. You work with made up numbers to understand the problem. Then you can make conclusions even without knowing the precise numbers.
For example, my analysis doesn't change much wheter the rate of lawsuits is 0.1% or 0.01% or 0.001%. It would change if the rate of lawsuits is 1%.
But I am pretty sure that the rate of lawsuits after interview rejection is much less than 1%. So I can make a conclusion without knowing precise numbers.
Calculations based on estimates come up all the time, and they are very valuable. They make it clear what assumptions your decisions are based on.
What's the alternative? You have to make a decision. If you don't want to use estimates, what are you going to base your decision on? Whatever feels right?
What I'm saying is that even with the lawsuit rate that low, there is no real incentive for the company (really the people sending the emails) to behave otherwise than they already do. Actually their benefit is that low, that even a slight error of that 0.1% guess would make the whole do-good business a really bad proposition.
Your (guess) data is probably right but it discounts too much the downsides. When you present your hypothesis to them (e.g. me) they will tell you (rightly so) to try it yourself first.
The incentive is “be a good person/entity.” This whole bottom-line approach and “what’s in it for me” is unfortunate to say the least. Behind every corporate establishment is a cadre of people. People with (possibly) spouses, children, non-deceased parents, neighbors, and friends. Possibly at some abstract level similar to the abstract “us.” Treat people like people, not some kind of legal liability.
