Stochastic

In statistics, stochastic refers to any process, model, or system that involves randomness or uncertainty. A stochastic process is one where outcomes are not fully determined but instead have a probabilistic structure—future values or events depend partly on chance.

Stochastic is often used to describe models that incorporate random variables or where the same input may lead to different outputs across trials. Analysts use these models when outcomes are influenced by inherent variability that cannot be predicted with certainty, even if we know the underlying rules or distributions.

This term appears frequently in probability theory, econometrics, machine learning, and fields like finance, biology, and physics. Common examples include stochastic simulations, stochastic differential equations, and stochastic processes like Markov chains.

For example, stock price movements are typically modeled as stochastic processes because they evolve over time with an element of randomness. While analysts may estimate probabilities of future movements based on trends or volatility, the actual path the stock takes cannot be precisely predicted.