Una variable aleatoria o variable estocástica es una función que asigna un valor, usualmente numérico, al resultado de un experimento aleatorio. Por ejemplo, los posibles resultados de tirar un dado dos veces: (1, 1), (1, 2), etc. o un número real (p.e., la temperatura máxima medida a lo largo del día en una ciudad concreta). Los valores posibles de una variable aleatoria pueden representar los posibles resultados de un experimento aún no realizado, o los posibles valores de una cantidad cuyo valor actualmente existente es incierto (p.e., como resultado de medición incompleta o imprecisa). Intuitivamente, una variable aleatoria puede tomarse como una cantidad cuyo valor no es fijo pero puede tomar diferentes valores; una distribución de probabilidad se usa para describir la probabilidad de que se den los diferentes valores. En términos formales una variable aleatoria es una función definida sobre un espacio de probabilidad. Las variables aleatorias suelen tomar valores reales, pero se pueden considerar valores aleatorios como valores lógicos, funciones o cualquier tipo de elementos (de un espacio medible). El término elemento aleatorio se utiliza para englobar todo ese tipo de conceptos relacionados. Un concepto relacionado es el de proceso estocástico, un conjunto de variables aleatorias ordenadas (habitualmente por orden o tiempo).

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In probability and statistics, a random variable, random quantity, aleatory variable or stochastic variable is a quantity whose value depends in some clearly-defined way on a set of possible random events. A random variable can take on a set of possible different values (similarly to other mathematical variables), each with an associated probability. Like a traditional mathematical variable, its value is thus unknown a priori (before the outcome of the events is known). However, in a more abstract sense, it is defined as a function that maps from an outcome of the events (that is, from a point in a probability space) to a mathematically convenient outcome label, usually a real number. In this sense, it is a procedure for assigning a number to an outcome, and paradoxically this procedure itself is neither random nor variable. The function which characterizes a random variable must also be measurable, which rules out certain pathological cases such as those in which the random variable's quantity is infinitely sensitive to any small change in the outcome. A random variable's possible values might represent the possible outcomes of a yet-to-be-performed experiment, or the possible outcomes of a past experiment whose already-existing value is uncertain (for example, due to imprecise measurements or quantum uncertainty). They may also conceptually represent either the results of an "objectively" random process (such as rolling a die) or the "subjective" randomness that results from incomplete knowledge of a quantity. The meaning of the probabilities assigned to the potential values of a random variable is not part of probability theory itself but is instead related to philosophical arguments over the interpretation of probability. The mathematics works the same regardless of the particular interpretation in use. A random variable has a probability distribution, which specifies the probability that its value falls in any given interval. Random variables can be discrete, that is, taking any of a specified finite or countable list of values, endowed with a probability mass function characteristic of the random variable's probability distribution; or continuous, taking any numerical value in an interval or collection of intervals, via a probability density function that is characteristic of the random variable's probability distribution; or a mixture of both types. Two random variables with the same probability distribution can still differ in terms of their associations with, or independence from, other random variables. The realizations of a random variable, that is, the results of randomly choosing values according to the variable's probability distribution function, are called random variates. The formal mathematical treatment of random variables is a topic in probability theory. In that context, a random variable is understood as a function defined on a sample space whose outputs are numerical values.

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