Expected Value Calculator
List the possible outcomes and how likely each one is, and get the expected value (the long-run average), the variance and the standard deviation. Probabilities can be decimals, percentages or fractions, or you can enter counts and let the calculator scale them.
Each line: optional name, then the outcome value, then its probability (0.25, 25% or 1/4). Don't use thousands separators.
Expected total and spread over repeated independent trials
How to use the Expected Value Calculator
- Enter one outcome per line: an optional name, the value, then its probability, e.g. Win, 100, 0.25.
- Choose how the probabilities are given: exact probabilities that add up to 1, or weights/counts to be scaled.
- Optionally set a number of trials to see the expected total.
- Read E(X), the variance and the standard deviation.
- Check the table of x·P(x) terms and the chart, or copy the results.
Expected value formulas
| Quantity | Formula |
|---|---|
| Expected value (mean) | E(X) = μ = Σ x · P(x) |
| Variance | Var(X) = Σ (x − μ)² · P(x) = E(X²) − μ² |
| Standard deviation | σ = √Var(X) |
| Linear change | E(aX + b) = a·E(X) + b, Var(aX + b) = a²·Var(X) |
| N independent trials | E(total) = N·μ, SD(total) = √N·σ |
Worked example: a fair six-sided die
| x | P(x) | x · P(x) | (x − 3.5)² · P(x) |
|---|---|---|---|
| 1 | 1/6 | 0.1667 | 1.0417 |
| 2 | 1/6 | 0.3333 | 0.3750 |
| 3 | 1/6 | 0.5000 | 0.0417 |
| 4 | 1/6 | 0.6667 | 0.0417 |
| 5 | 1/6 | 0.8333 | 0.3750 |
| 6 | 1/6 | 1.0000 | 1.0417 |
| Σ | 1 | 3.5 | 2.9167 |
The expected roll is 3.5, a value no single roll can show, and the standard deviation is √2.9167 ≈ 1.7078.
Frequently asked questions
What is expected value?
The expected value E(X) of a random variable is the probability-weighted average of all its possible outcomes: E(X) = Σ x · P(x). It is the average result you would get per trial if you repeated the experiment many times; a single trial need not ever produce that value.
How do you calculate expected value?
Multiply each outcome by its probability and add the products. For a fair die, E(X) = (1 + 2 + 3 + 4 + 5 + 6) × 1/6 = 3.5. The probabilities of all outcomes must add up to 1.
How do you find the variance and standard deviation of a probability distribution?
Variance is Var(X) = Σ (x − μ)² · P(x), or equivalently E(X²) − μ², where μ is the expected value. The standard deviation is its square root. For a fair die the variance is 35/12 ≈ 2.9167 and the standard deviation ≈ 1.7078.
What if my probabilities don't add up to 1?
Valid probabilities must sum to exactly 1. If yours are counts, frequencies or relative weights, choose "Weights / counts" and the calculator divides each by the total. Tiny rounding differences (such as 0.333 + 0.333 + 0.333) are rescaled automatically.
How is expected value used in decisions?
It compares options with uncertain outcomes, for example business projects, insurance or pricing: the option with the higher expected value pays more on average. Because it ignores risk, also look at the standard deviation and the chance of a loss before deciding.
What is the expected value over many trials?
For N independent trials the expected total is N × E(X) and its standard deviation is √N × σ. The total grows N times but its spread grows only √N times, which is why averages become more predictable over many trials.