Human reasoning is systematically shaped by cognitive biases, mental shortcuts, and predictable errors that can distort even careful thinking. Confirmation bias leads us to search for, interpret, and remember information that confirms preexisting beliefs while discounting contrary evidence. Anchoring bias makes us rely too heavily on the first piece of information we encounter. The availability heuristic causes us to judge the likelihood of events by how easily examples come to mind, often overestimating vivid or recent occurrences. The Dunning–Kruger effect describes how people with low ability tend to overestimate their competence, while highly skilled individuals may underestimate theirs.
Other biases shape how we update beliefs and make decisions. Cognitive dissonance produces discomfort when we hold contradictory beliefs, often prompting rationalization rather than revision. Hindsight bias makes past events seem more predictable than they were—the so-called "I knew it all along" effect. The framing effect shows that people react differently depending on how information is presented, even when the underlying facts are identical, such as "90% survival rate" versus "10% mortality rate." Groupthink occurs when the desire for harmony in a group overrides realistic appraisal of alternatives. The sunk cost fallacy persuades us to continue failing endeavors because of past investment rather than current value, and motivated reasoning pushes us toward conclusions we want to be true.
Probability judgments are particularly vulnerable to predictable errors. The gambler's fallacy wrongly assumes that past random events affect future ones—for instance, believing that a coin is "due" for heads after a streak of tails—while in reality each independent trial is unaffected by previous outcomes. The base rate fallacy ignores how common an event actually is in the general population, focusing instead on less informative specific evidence. Bayesian reasoning counteracts some of these errors by systematically updating the probability of a hypothesis in light of new evidence, using Bayes' theorem \(P(H \mid E) = \frac{P(E \mid H) \times P(H)}{P(E)}\). Finally, the self-fulfilling prophecy illustrates how expectations can themselves alter outcomes, since believing a prediction often changes the behavior that brings it about.