Using Confusion Matrices to Quantify the Cost of Being Wrong

There are so many confusing and sometimes even counter-intuitive concepts in statistics. I mean, come on…even explaining the differences between Null Hypothesis and Alternative Hypothesis can be an ordeal. All I want to do is to understand and quantify the cost of my analytical models being wrong. For example, let's say that I'm a shepherd who has bad eyesight and have a hard time distinguishing between a wolf and a sheep dog. That's obviously a bad trait, because the costs of being wrong are very expensive: Okay, so I'm not a very good shepherd, but I am a very sophisticated shepherd and I've build a Neural Network application to distinguish a sheep dog from a wolf.