[jupiter90000]

I hear what you're saying, but in terms of usefulness for business decisions, what leaders at a company would be satisfied with someone providing Vapnik's quote? Certainly machine learning and statistics have applications outside of business, but when it comes to realities in industry settings, very often an explanation of results is necessary, in addition to an explanation of why a particular machine learning approach is best for solving a given problem, how it works to solve the problem, etc.

[p4wnc6]

The problem is not about how to take a scientific conclusion and make it suitably less scientific to serve as a business explanation.

The problem is to replace inept employees who believe "business decisions" are not scientific questions, so that over time there is a convergence to using the scientific method, with legitimate statistical rigor, when making a so-called business decision.

Generally speaking, the only people who want for there to be a distinction between a "business question" and a "scientific question" are people who can profit from the political manipulation that becomes possible once a question is decoupled from technological and statistical rigor. Once that decoupling happens, you can use almost anything as the basis of a decision, and you can secure blame insurance against almost any outcome.

This is why many of the experiments testing whether prediction markets, when used internally to a company, can force projects to be completed on time and under budget are generally met with extreme resistance from managers even when they are resounding successes.

The managers don't care if the projects are delivered on time or under budget. What they care about is being able to use political tools to argue for bonuses, create pockets of job security, backstab colleagues, block opposing coalitions within the firm. You can't do that stuff if everyone is expected to be scientific, so you have to introduce the arbitrary buzzword "business" into the mix, and start demanding nonsense stuff like "actionable insight" -- things that are intentionally not scientifically rigorous to ensure there is room for pliable political manipulation for self-serving and/or rent-seeking executives, all with plausible deniability that it's supposed to be "quantitative."

[jupiter90000]

Edit: I wrote prior to this edit agreeing with alot of what you said. However, I mostly wanted to say that providing an explanation that people may be able to understand more easily doesn't necessarily make a scientific conclusion less scientific.

[p4wnc6]

True. It's not necessary. But most times in real life when this happens, the scientific rigor is the first thing to go. This can manifest itself in a lot of ways. One way is an explicit mandate to find that a pre-determined conclusion is supported, even if the data don't support it. Another way is to place a greater emphasis on speed of delivery than on accuracy, and to avoid quantifying the true trade-off between the two by invoking the magic buzzword "business concerns" or "bottom line" or whatever.

Yet another way is that data analytics platforms are built from the ground up with hard-wired priorty given to scaling out the ability to test multiple hypotheses without any attempt to correct the significance metrics for the multiplicity of testing (or, even subtler, for subject researcher degrees of freedom that further affect the multiplicity of testing). Often, the business stakeholders who are demanding such an "analytics" system aren't even aware of the statistical fallacies they are inexorably baking right into the platform itself (one might call this the "Hadoop disease", though it's not stricly the fault of Hadoop or Hadoop-like tools).

At any rate, I would say in the current climate of "analytics" in business environments, to a good first approximation, one can assume that "make it easy to understand" is exactly equivalent to "throw out any and all difficult yet rigorous science until the thing is cheap and easy, and then just use that."

[jupiter90000]

Thanks for your perspective. It is disheartening to see what you're talking about happen. I've definitely witnessed this first-hand and it was frustrating to me to essentially see lots of rigorous scientific work be dismissed because of someone's ego or because someone making the decisions couldn't wrap their brain around it.

What I've seen around this is analytics professionals hired under the pretense that their skills to produce accurate scientific conclusions will be used for the good of a business, yet having their conclusions and efforts dismissed for no good reason other than decision makers 'didn't get it' or otherwise just refused to heed the results. So why did they hire experts in the first place, then? To lend the company credibility that it doesn't really deserve? I'm sure lots of the reason for this type of thing is politically motivated, as you previously mentioned.