Every day, we read the weather forecast, and see the probabilities assigned to predictions of future events-eighty percent chance of rain, etc. The National Weather Service issues about 10 million weather forecasts each year, and this gives them lots of information that they can use to see how well they are doing. Of equal importance, it gives the public lots of opportunities to evaluate the predictions as well, and develop expectations that can guide actions. No one expects weather predictions to be perfect, and one's experience with past predictions allows one to make decisions in response to new predictions. For example, during the muggy summer months in Washington, DC, weather forecasts routinely show a chance of thunderstorms in the afternoon, but experience quickly teaches that this is not sufficient reason to cancel a picnic.
Data are no substitute for experience.
It might seem logical that the more data and the more sophisticated mathematics that go into a prediction, the more accurate the prediction will be, but this is simply not the case. But it is often impossible to know what the most important data are, or if they have been taken into account in making the prediction. Economic forecasts, for example, are supported by voluminous data processed through some of the world's fastest computers, but experience shows that they are often little better than random guesses. One of the most significant predictions made by scientists in recent years is that human emission of greenhouse gases will cause the Earth's atmospheric temperature to rise by several degrees during the 21st century. But this prediction, while supported by billions of dollars of advanced research, is very difficult to evaluate.