1. Enter possible outcomes
Use positive numbers for gains, negative numbers for losses, and zero for break-even outcomes.
Calculate the probability-weighted average of possible outcomes, measure variance and standard deviation, review gain and loss probabilities, and project the expected total across repeated trials.
Enter each outcome and probability. Probabilities must total 100% or 1.00.
Each outcome is multiplied by its probability. The weighted contributions are added to calculate expected value, while variance and standard deviation measure how widely possible outcomes are spread around that average.
Use positive numbers for gains, negative numbers for losses, and zero for break-even outcomes.
Enter percentages totalling 100% or decimals totalling 1.00.
Compare expected value with standard deviation, outcome range, and gain or loss probability.
E(X) = Σ [Outcome × Probability]
Var(X) = Σ [Probability × (Outcome − E(X))²]
Expected Total = E(X) × Number of Trials
Review downside size, liquidity, time horizon, variability, and the reliability of the probability estimates.
Repeated participation is expected to lose value unless missing benefits or outcomes materially change the model.
Results may differ greatly from the expected value. Review rare outcomes, risk limits, and diversification.
Confirm that outcomes are mutually exclusive and collectively exhaustive, then correct rounding or omissions.
Important details for calculating and interpreting expected value.
Expected value is the probability-weighted average of all possible outcomes. It represents the long-run average result across many repetitions.
Multiply every outcome by its probability, then add all weighted contributions.
Yes. They should total 100% in percentage format or 1.00 in decimal format.
Yes. A negative result means the probability-weighted average outcome is below zero.
No. It describes a long-run average, not the guaranteed result of one attempt.
They describe variability and help distinguish decisions with similar expected values but different risk.
It is the single-trial expected value multiplied by the number of comparable repetitions.
It is used in probability, finance, insurance, games, forecasting, investing, business analysis, and risk management.