• Choose a category
• All
• Classic-Fiction

This tendency, which psychologists call creeping determinism, is related to the better-known phenomenon of hindsight bias, the after-the-fact tendency to think that we “knew it all along.” In a variety of lab experiments, psychologists have asked participants to make predictions about future events and then reinterviewed them after the events in question had taken place. When recalling their previous predictions, subjects consistently report being more certain of their correct predictions, and less certain of their incorrect predictions, than they had reported at the time they made them. Creeping determinism, however, is subtly different from hindsight bias and even more deceptive. Hindsight bias, it turns out, can be counteracted by reminding people of what they said before they knew the answer or by forcing them to keep records of their predictions. But even when we recall perfectly accurately how uncertain we were about the way events would transpire—even when we concede to have been caught completely by surprise—we still have a tendency to treat the realized outcome as inevitable. Ahead of time, for example, it might have seemed that the surge was just as likely to have had no effect as to lead to a drop in violence. But once we know that the drop in violence is what actually happened, it doesn’t matter whether or not we knew all along that it was going to happen (hindsight bias). We still believe that it was going to happen, because it did.3

SAMPLING BIAS

Creeping determinism means that we pay less attention than we should to the things that don’t happen. But we also pay too little attention to most of what does happen. We notice when we just miss the train, but not all the times when it arrives shortly after we do. We notice when we unexpectedly run into an acquaintance at the airport, but not all the times when we do not. We notice when a mutual fund manager beats the S&P 500 ten years in a row or when a basketball player has a “hot hand” or when a baseball player has a long hitting streak, but not all the times when fund managers and sportsmen alike do not display streaks of any kind. And we notice when a new trend appears or a small company becomes phenomenally successful, but not all the times when potential trends or new companies disappear before even registering on the public consciousness.

Just as with our tendency to emphasize the things that happened over those that didn’t, our bias toward “interesting” things is completely understandable. Why would we be interested in uninteresting things? Nevertheless, it exacerbates our tendency to construct explanations that account for only some of the data. If we want to know why some people are rich, for example, or why some companies are successful, it may seem sensible to look for rich people or successful companies and identify which attributes they share. But what this exercise can’t reveal is that if we instead looked at people who aren’t rich or companies that aren’t successful, we might have found that they exhibit many of the same attributes. The only way to identify attributes that differentiate successful from unsuccessful entities is to consider both kinds, and to look for systematic differences. Yet because what we care about is success, it seems pointless—or simply uninteresting—to worry about the absence of success. Thus we infer that certain attributes are related to success when in fact they may be equally related to failure.

This problem of “sampling bias” is especially acute when the things we pay attention to—the interesting events—happen only rarely. For example, when Western Airlines Flight 2605 crashed into a truck that had been left on an unused runway at Mexico City on October 31, 1979, investigators quickly identified five contributing factors. First, both the pilot and the navigator were fatigued, each having had only a few hours’ sleep in the past twenty-four hours. Second, there was a communication mix-up between the crew and