Expected value of a discrete random variable
Expected value weights possible values by their probabilities.
Key knowledge
For a discrete real-valued random variable X with probabilities p(x), its finite expected value is Σx xp(x) when Σx |x|p(x) is finite. The expected value need not itself be a possible observed value.
Values and equations
E[X] = Σₓ x p(x)
- X
- Discrete real-valued random variable
- x
- Value in the support of X
- p(x)
- Probability P(X=x)
- E[X]
- Expected value if Σₓ |x|p(x)<∞
Conditions and scope
- Sum over the finite or countable support and require absolute convergence for a finite value.
- An expected value describes the probability distribution, not a guaranteed result of one trial.
Sources · 2
References
Sources retain their recorded scope. A source link alone does not establish verification of every statement.
Evidence and review
Discrete weighted mean and finite-existence condition.
2026-10-10 · Checked positive/negative-part discussion and discrete examples; no unrestricted convergence claim.
Foundational review
2026-10-10 · Fresh primary-source inspection of the complete compact record, including every statement, equation, quantity and material condition, followed by independent AI editorial review. This check does not certify all linked lessons or every passage of every cited publication.
Sources from other places
Agreement within this scope
Discrete expected value as the absolutely convergent probability-weighted sum.
The notes distinguish undefined sums and allow extended positive infinity; the repository states the finite case.
University of Cambridge, Statistical Laboratory
- Probability · Axioms, conditioning and expectation United KingdomEnglish
§7.3, pp.27–28 (PDF pp.34–35)
- Probability · Axioms, conditioning and expectation
Entry details
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- math-expectation
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- core.1
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- Essential core · first collection