Expected Value Calculator
Calculate expected value (mean) of a probability distribution. E(X) = Σ x⋅P(x). Find expected value for discrete distributions, games of chance, and decision analysis.
Formula & Reference
| Variable | Symbol | Formula | Units |
|---|---|---|---|
| Expected Value Calculator | — | E(X) = Σ xi × P(xi) | same as outcome units |
Step-by-Step Examples
Win $10 if heads (p=0.5), lose $5 if tails (p=0.5).
- E(X) = 10×0.5 + (-5)×0.5
- = 5 - 2.5 = 2.50
- Positive expected value: play this game!
- In 100 games, expect to win ~$250 total
Win $1000 (p=0.001), Win $10 (p=0.05), Lose $5 (p=0.949). Ticket costs $5.
- E(X) = 1000×0.001 + 10×0.05 + (-5)×0.949
- = 1.00 + 0.50 - 4.745 = -3.245
- Negative expected value — lose $3.25 per ticket on average
No insurance: lose $10,000 if disaster (p=0.02), $0 otherwise (p=0.98). Premium = $150.
- E(loss without insurance) = 10000×0.02 = $200
- E(cost with insurance) = $150 (certain)
- Insurance saves $50 in expectation PLUS eliminates catastrophic risk
- Expected value slightly favors insurance here
Real-World Applications
Common Mistakes to Avoid
All P(xi) must sum to 1.0 for a valid probability distribution. Check your probabilities.
E(X) is the long-run average — it may never actually occur. A coin flip has E=0.5 but only gives 0 or 1. Expected value is a theoretical average, not a prediction of one outcome.
Two choices with E=100: Choice A: always $100. Choice B: $200 with p=0.5 or $0 with p=0.5. Same expected value, very different risk. Risk aversion means people often prefer A.
Frequently Asked Questions
Related Math Calculators
Formula Explorer connections
Interpretation: This formula summarizes data, models uncertainty or supports inference about a population or random process. Assumption: The sampling design and distribution assumptions must match the data. Independence, sample size, outliers and measurement quality can materially affect interpretation.