The whole is less than the sum of its parts… This is the tendency to estimate the probability of a complex event as lower than the compiled probabilities of the simple events that compose it.
I.e. judging the probability of dying from natural causes as lower than the sum of the probabilities of dying from each individual natural cause.
Not too hot, not too cold, but just right… The Regressive Bias describes our tendency to attribute causality to changes that occur because of natural fluctuations in variance. This is strongly related to the concept of “regression to the mean”, which simply indicates that while most observations have a range of variance (e.g., I may eat a lot, or a little food), they generally vary around a given average that observation will trend towards (i.e., there is an average amount of food that I will usually consume).
It’s gonna be fine! The Normalcy Bias describes our tendency to underestimate and minimize both the probability of negative events actually happening, and the extent to which their consequences will affect us.
For example: individuals in a coastal town may fail to prepare for a major hurricane because they expect events today to be the same as yesterday.
It’s definitely gonna be tails this time… Even for events with a fixed probability (e.g., flipping a coin, 50/50), we have a tendency to believe that future probabilities (e.g., the odds of flipping tails) increase or decrease due to past events (e.g., flipping heads 10 times in a row).
For example: if you role a die and get 3 multiple times you might think that the next time you role the die the odds of getting a 3 are less than 1/6 but it in fact remains 1/6.
This looks familiar…The Clustering Illusion describes our tendency to see patterns even in random sequences of numbers or events. This can lead to erroneous predictions of future states, such as when we believe that a basketball player has a “hot hand” and just can’t miss.
For example, when examining a scatter plot of the market returns of the S&P500 you might see a pattern when there is in fact no pattern.
Is Mt. Everest more that 12,000 meters high? Judgments and estimations can be influenced by arbitrarily suggested values (anchors).
For example, a restaurant menu may put a profitable item ($20 omelet) next to a very expensive option ($500 steak). While $20 is expensive for an omelet it is much cheaper than the $500 steak. In this case the anchor (the $500 steak) causes the restaurant guest to compare the two and reason that the omelet doesn’t look so expensive after-all when compared to the steak.
Just play it safe… The Ambiguity Effect describes our tendency to avoid options for which the probability seems unknown. This can cause to make conservative decisions and avoid risk in novel situations.
For example, when deciding between two hotels, you may be more likely to go with the hotel that has an average review rather than the other hotel which just recently opened and is has unknown quality.