👉 Conviction math, a term often used in the context of legal systems, particularly in the United States, refers to the mathematical framework used to assess the strength of evidence and determine the likelihood that a defendant is guilty beyond a reasonable doubt. It involves calculating the probability of observing the evidence (or more extreme evidence) if the defendant's guilt were true, given the known probabilities of the evidence itself and any other relevant factors. This is often expressed using Bayes' theorem, which updates the probability of a hypothesis (in this case, the defendant's guilt) based on new evidence. The goal is to ensure that the probability of the defendant being guilty, given the evidence, is sufficiently low (typically below 50%) to justify a conviction. This mathematical approach helps to balance the need for justice with the risk of wrongful convictions, ensuring that convictions are based on strong, reliable evidence.
