Final answer:
Gambler's Fallacy describes the incorrect belief that past random events affect the probability of future events in probabilistic situations. It is based on the incorrect assumption that if an event has occurred repeatedly, the opposite event is more likely to occur next, which doesn't apply to independent events like coin flips.
Step-by-step explanation:
The belief that prior outcomes can influence the outcome of probabilistic events describes the Gambler's Fallacy. This is the mistaken belief that if something happens more frequently than normal during a given period, it will happen less frequently in the future, or vice versa when it comes to independent events.
For example, flipping a coin and getting a string of heads does not increase the likelihood of getting tails on the next flip; each flip is statistically independent and has an equal chance of resulting in heads or tails.
In contrast, we have concepts like regression to the mean, which suggests that extreme outcomes tend to be followed by more moderate ones, the law of large numbers, which states that as the number of trials increases, the empirical probability approaches the theoretical probability, and the availability heuristic, which refers to making decisions based on the information most readily available to us, rather than all possible data.