Skip to content
Toolcroft

Math Calculators

Probability Calculator - Single, Complement, And, Or

Calculate probability for single events, complements, union (A or B), and intersection (A and B). Results shown as fraction, decimal, and percentage.

Operation

Probability

0.500000

Fraction: 1/2Percentage: 50.0000%

Formula

P(A) = 0.5

Explanation

The probability of event A occurring is 50.0000%.

Probability: 0.50000.5000Probability0.00001.0000Medium
Probability
FieldValue
Current value0.5000
Range0.0000 – 1.0000
CategoryMedium

What is probability?

Probability measures how likely an event is to occur, expressed as a number from 0 (impossible) to 1 (certain). A probability of 0.5 means a 50% chance. This calculator handles the four most common probability operations taught in statistics and probability courses.

Single event probability

Enter P(A) directly as a decimal. For example, rolling a 6 on a fair die has probability 1/6 ≈ 0.1667. The calculator displays the result as a fraction, decimal, and percentage.

Complement: P(A′)

The complement of event A is the probability that A does not occur: P(A′) = 1 − P(A). If there is a 25% chance of success, there is a 75% chance of failure.

P(A or B): Union

The union is the probability that at least one of A or B occurs. For mutually exclusive events (they cannot both occur at the same time): P(A ∪ B) = P(A) + P(B). For non-mutually exclusive independent events: P(A ∪ B) = P(A) + P(B) − P(A)·P(B).

P(A and B): Intersection

The intersection is the probability that both A and B occur. For independent events: P(A ∩ B) = P(A) × P(B). For dependent events, enter P(B|A): the conditional probability of B given A: P(A ∩ B) = P(A) × P(B|A).

Bayes' theorem

Bayes' theorem updates the probability of an event given new evidence:

P(A|B) = P(B|A) × P(A) ÷ P(B)

Classic example: a medical test for a rare disease (prevalence 1%) has 99% sensitivity and 99% specificity. If you test positive, the probability you actually have the disease is only ~50% — not 99%. This base rate neglect fallacy is why Bayes’ theorem matters for interpreting test results and statistical evidence.

Common probability mistakes

  • Gambler’s fallacy: believing that past independent outcomes affect future ones. A coin that has landed heads 10 times in a row still has a 50% chance of heads on the next flip — the coin has no memory.
  • Prosecutor’s fallacy: confusing P(evidence | innocent) with P(innocent | evidence). A 1-in-a-million DNA match probability does not mean the defendant has only a 1-in-a-million chance of being innocent — base rates matter.

Worked examples

ScenarioP(A)P(B)P(A and B)P(A or B)
Two coin flips (both heads)0.50.50.250.75
Two dice, both roll 61/6 ≈ 0.1671/6 ≈ 0.1671/36 ≈ 0.028~0.306
Draw ace then king from a deck4/52 ≈ 0.0774/51 ≈ 0.078~0.006~0.149