Probability Calculator

Find the probability of an event from favourable and total outcomes, assuming every outcome is equally likely.

How many outcomes count as the event, for example 3.

How many outcomes are possible in all, at least 1, for example 10.

Assumes every outcome is equally likely, as with a fair coin, die or draw. Whole numbers only.

Worked example

Inputs: 1 favourable outcome (rolling a 6) out of 6 total outcomes, all equally likely. Formula: P = favourable ÷ total. Substitution: P = 1 ÷ 6. Result: P = 1/6 ≈ 0.166667, or about 16.6667%. The complement, the chance of not rolling a 6, is 1 − 1/6 = 5/6.

How it works

When every outcome is equally likely, the probability of an event is the number of outcomes that count as the event divided by the number of possible outcomes: P = favourable ÷ total. It runs from 0 (impossible) to 1 (certain). The formula is only valid under that equal-likelihood assumption; it does not apply to a loaded die or to outcomes with different chances. The exact fraction is shown in lowest terms, and the decimal and percentage are exact when they end (3/10 = 0.3 = 30%) and rounded to 6 significant figures with a leading "≈" when they repeat (1/6 ≈ 0.166667). The complement 1 − P is the probability that the event does not happen.

This calculator handles a single event from counts. It does not combine events with "and" or "or", assume independence, work out conditional probabilities or Bayes' theorem, or use distributions such as the binomial or normal. To count the outcomes first, the Combination and Permutation calculators give the number of selections and arrangements.

What this result means

The probability is the share of the possible outcomes that count as the event, from 0 (impossible) to 1 (certain). The percentage is the same figure out of 100, and the complement is the chance the event does not happen.

Common mistakes

  • Using the formula when the outcomes are not equally likely. Favourable ÷ total only works for a fair coin, a fair die, a random draw and similar cases; a loaded die or a weighted spinner needs different information.
  • Counting outcomes wrongly. Rolling two dice has 36 equally likely outcomes, not 11 totals, because the totals are not equally likely.

Assumptions and limitations

  • One event from whole-number counts. There is no combining of events with and or or, no conditional probability or Bayes theorem, and no binomial or normal distributions.
  • The fraction is exact. The decimal and percentage are exact when they end and rounded to 6 significant figures, with a leading ≈, when they repeat.

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