We are all well aware of the predictive analytical capabilities, however, it remains important to stress that the outcome of a prediction model will always contain an error. Decision makers need to know how big that error is.  “Now that predictive modelling is getting used in environments where errors can have a large impact, this should be top of mind for both the analytics professional and the decision maker. Just imagine your doctor concluding that your liver needs to be taken out because his predictive model estimates a high probability of a very nasty decease? Wouldn’t your first question be how certain he/she is about that prediction? So, my advice to decision makers, only use outcomes of predictive models if accuracy and precision measures are provided. If they are not there, ask for them. Without them, a decision based on these predictions comes close to a blind leap of faith.” Read John Poppelaars’s post