In her book Mastermind: How to Think Like Sherlock Holmes, Maria Konnikova discussed four sets of circumstances that tend to make us overconfident:
- Familiarity — When we are dealing with familiar tasks, we feel somehow safer, thinking that we don't have the same need for caution as we would when trying something new. Each time we repeat something, we become better acquainted with it and our actions become more and more automatic, so we are less likely to put adequate thought or consideration into what we're doing.
- Action — As we actively engage, we become more confident in what we are doing. In one study, individuals who flipped a coin themselves, in contrast to watching someone else flip it, were more confident in being able to predict heads or tails accurately, even though, objectively, the probabilities remained unchanged.
- Difficulty — We tend to be under-confident on easy problems and overconfident on difficult ones. This is called the hard-easy effect. We underestimate our ability to do well when all sign all signs point to success, and we overestimate it when the signs become less favorable.
- Information — When we have more information about something, we are more likely to think we can handle it, even if the additional information doesn't actually add to our knowledge in a significant way.
All four can affect decision making, but the last circumstance should make us circumspect about big data.
Additional information doesn't always add knowledge. Sometimes it adds meaningless correlations or doubles down on dubious connections. Other times, its confirming information could be based on systematic errors or could be the basis for telling a better story—one that you can be more confident in retelling.
Overconfidence creates the illusion of movement, running in place as if on a treadmill, where although you’re moving you aren't going anywhere. When you analyze more data, it should move you toward making better, more data-driven, decisions. If instead it only confirms what you already believe, then you may be data-driving in place on the treadmill of overconfidence.