How do you overcome results oriented thinking from a client? I am working with a client on a special project that I am going to obfuscate in this question. Basically, I'm trying to overcome some short-term, results-oriented thinking from my client. 

Let's say you have a model to forecast the performance of a racehorse. Your model tells your client to sell racehorse X because the probability of its performance is low (&lt;10%). The horse is sold and said racehorse goes on to win 3 races. Your client says, "See, we should have never let go of that racehorse! The model is wrong!"

As data scientist we can understand that anomalies happen and that this horse may have also lost some races - we were just on the wrong side of probability this time. But how do you overcome those objections with the client? How do you turn short-term thinking into a long-term outlook of predictive modeling?
The scenario you described with one horse is basically anecdotal evidence. To objectively evaluate what you're doing, you need to agree with the client to make this an experiment. 


  I understand you don't trust my horse predictions and that's totally
  understandable. It is human nature to worry about the ones that got
  away. Let's handle this objectively and run an experiment. I'll give
  you buy/sell ratings on 50 horses. We then track their performance for
  six months. If on average my ratings outperform a coin flip, we'll
  know we're onto something at a macro level even if a few got away. If
  my models end up being worse than a coinflip, we'll know the model
  needs to be improved or you need to fire me.