Expected Value Calculator

Calculate expected value EV = Σ(p × v) for 3–5 discrete outcomes. Includes variance, standard deviation, risk-adjusted EV, scenario comparison, maximin/maximax decision rules, and gambler's fallacy warning.

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Expected Value (EV)
Probability Sum
Interpretation
Extended More scenarios, charts & detailed breakdown
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Expected Value
Variance
Standard Deviation
Professional Full parameters & maximum detail
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EV & Distribution

Expected Value
Variance
Standard Deviation

Decision Theory

Risk-Adjusted EV (EV − λσ)
Maximin Value (worst case)
Gambler's Fallacy Warning

How to Use This Calculator

  1. Enter probability % and value for each outcome (probabilities should sum to 100%).
  2. EV = Σ(p × v) is computed instantly.
  3. Use the 5 Outcomes tab for more complex distributions.
  4. Use Compare 2 Scenarios to find the higher-EV option.
  5. Use Professional for variance, risk-adjusted EV, and decision theory metrics.

Formula

EV = Σ(pᵢ × vᵢ)  |  Variance = Σ[pᵢ × (vᵢ − EV)²]  |  SD = √Variance

Risk-Adjusted EV = EV − λ × σ

Example

50% × $100 + 30% × $0 + 20% × (−$50) = $50 + $0 − $10 = EV $40. Variance = 0.5(100−40)² + 0.3(0−40)² + 0.2(−50−40)² = 1800+480+1620 = 3900. SD ≈ $62.45.

Frequently Asked Questions

  • Expected value (EV) = Σ(probability × value) for all outcomes. It is the long-run average outcome if you repeated the same random event many times. EV > 0 means favorable; EV < 0 means unfavorable.
  • EV = P1×V1 + P2×V2 + P3×V3, where probabilities must sum to 1 (100%). Example: 50% chance of +$100, 30% of $0, 20% of −$50: EV = 0.5(100) + 0.3(0) + 0.2(−50) = 50 − 10 = $40.
  • Variance = Σ[p × (v − EV)²]. It measures how spread out outcomes are around the expected value. High variance means outcomes vary widely even if EV is positive. Standard deviation = √Variance.
  • Risk-adjusted EV = EV − λ×σ, where λ is a risk-aversion factor and σ is standard deviation. A risk-neutral investor uses λ=0; a risk-averse person uses λ=0.5–1.0, penalizing high-variance choices.
  • The gambler's fallacy is the false belief that past independent outcomes affect future ones. If a fair coin lands heads 10 times, the next flip is still 50/50. Each trial is independent; past results don't change probabilities.

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Sources & References (5)
  1. MIT OCW 18.05 — Probability & Statistics — MIT OpenCourseWare
  2. Khan Academy — Expected Value — Khan Academy
  3. A First Course in Probability — Sheldon Ross — Pearson / Sheldon Ross
  4. Stanford CS109 — Probability for Computer Scientists — Stanford University
  5. Thinking Fast and Slow — Daniel Kahneman — Farrar, Straus and Giroux