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Probability calculators

Calculators for the chance of an event, the number of ways something can happen, and the probabilities a distribution assigns to each outcome. They are grouped into basic probability, distributions, dice and games, and diagnostic tests, so students, analysts and clinicians can go straight to the tool that fits their question.

Probability categories

Which calculator do I need?

You have or wantUse
Two events and you want P(A or B), P(A and B) or P(not A)Probability calculator
The chance of A once you know B has happened, from probabilities or a 2×2 tableConditional probability calculator
A prior and a test result, and you want the updated (posterior) probabilityBayes' theorem calculator
The number of ways to choose r items from n when order does not matterCombination calculator
The number of successes in n independent yes/no trialsBinomial distribution calculator
The number of events in a fixed time or space at a known average ratePoisson distribution calculator
A mean and SD, and the share of values above, below or between cut-offsNormal distribution calculator
The long-run average payoff of a bet or gameExpected value calculator

Events, counting and odds

The everyday questions: how likely an event is, how many ways it can happen, and what that means as odds.

Distributions, dice and tests

The distributions and applied tools people reach for most often.

How the probability calculators fit together

Most probability questions fall into one of four kinds. Basic probability covers single events and combinations of events: unions, intersections, conditional probability, Bayes' theorem, and the counting rules (combinations and permutations) that give you the numerator and denominator of a probability. Distributions describe a whole random variable at once, such as the number of successes in n trials or a measurement that follows a bell curve. Dice, coins and games apply those ideas to games of chance and to the classic puzzles that trip up intuition. Diagnostic tests and risk apply conditional probability to screening, classification and comparing groups.

A worked comparison: binomial or Poisson?

Suppose 3% of parts from a machine are faulty and you inspect 100. The exact model is binomial with n = 100 and p = 0.03. The binomial calculator gives P(X = 3) = 0.2275. The Poisson calculator with λ = np = 3 gives 0.2240, and both give P(X ≤ 3) = 0.6472 to four places. When n is large and p is small, the Poisson is a good shortcut; when p is near 0.5 or n is small, stay with the binomial.

Common mix-ups

  • P(A | B) is not P(B | A). The chance of a positive test given disease is the sensitivity; the chance of disease given a positive test also depends on how common the disease is. The Bayes' theorem calculator converts one into the other.
  • Odds are not probabilities. A probability of 0.2 is odds of 1 to 4 for, or 4 to 1 against. The odds calculator converts between the forms.
  • Combinations ignore order; permutations count it. Picking 3 people from 10 gives 120 combinations but 720 ordered arrangements.
  • Independent is not the same as mutually exclusive. Mutually exclusive events with non-zero probability can never be independent, because knowing one happened rules the other out.

Guides to read alongside

Common questions

What is the difference between probability and odds?
Probability is favourable outcomes divided by all outcomes, so it runs from 0 to 1. Odds compare favourable to unfavourable outcomes: a probability of 0.25 is odds of 1 to 3. Betting odds also include the bookmaker’s margin, which the implied probability calculator strips out.
When should I use a distribution calculator instead of the basic probability calculator?
Use the basic calculator when you have one or two events with known probabilities. Use a distribution calculator when you need the chance of a count or measurement taking a range of values, such as at least 3 successes in 10 trials or a height above 180 cm.
Do these calculators give exact answers?
The counting and discrete distribution calculators give exact results, using exact integer arithmetic where the numbers get large. Continuous distributions use standard numerical methods accurate to far more places than are displayed.