Outcome (Probability)
An outcome is a single possible result of a probability experiment.
Definition
An outcome is one specific result that can happen in an experiment: when you flip a coin, the possible outcomes are "heads" and "tails." An outcome represents an element of the sample space; outcomes are mutually exclusive (only one can occur per trial) and collectively exhaustive (one must occur), and for equally likely outcomes, $P(\text{each outcome}) = 1/n$, where $n$ is the total number of outcomes. Formally, an outcome is an element $\omega$ in the sample space $\Omega$; in Kolmogorov's framework, individual outcomes are not always events (measurable sets), depending on whether $\{\omega\}$ belongs to the sigma-algebra $\mathcal{F}$, though for discrete spaces every singleton is an event, while for continuous spaces singletons are events with measure zero.
Example
Rolling a single die has $6$ possible outcomes: $1$, $2$, $3$, $4$, $5$, or $6$, and each time you roll, exactly one of these outcomes occurs. Drawing one card from a standard $52$-card deck, each of the $52$ cards is a distinct outcome with probability $1/52$, and the event "draw an ace" is the set of $4$ such outcomes. In a probability model for a Poisson process, each outcome is an infinite sequence of event times $\{t_1, t_2, t_3, \ldots\}$, with the sample space $\Omega$ the set of all such sequences and events the measurable subsets of these sequences (e.g., "at least $3$ events in $[0,1]$").
Key Insight
Outcomes are the individual possibilities, and events are made up of one or more outcomes; knowing the outcomes helps you list the sample space and calculate probabilities, though when outcomes are not equally likely (e.g., a weighted die), you must assign probabilities directly and ensure they sum to $1$. The distinction between outcomes and events is crucial for continuous probability: we can ask for the probability of a range of outcomes (an interval) but not a single real-valued outcome, because $P(\{x\}) = 0$ for any $x$.