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.
Probability
0.500000
Formula
P(A) = 0.5
Explanation
The probability of event A occurring is 50.0000%.
| Field | Value |
|---|---|
| Current value | 0.5000 |
| Range | 0.0000 – 1.0000 |
| Category | Medium |
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
| Scenario | P(A) | P(B) | P(A and B) | P(A or B) |
|---|---|---|---|---|
| Two coin flips (both heads) | 0.5 | 0.5 | 0.25 | 0.75 |
| Two dice, both roll 6 | 1/6 ≈ 0.167 | 1/6 ≈ 0.167 | 1/36 ≈ 0.028 | ~0.306 |
| Draw ace then king from a deck | 4/52 ≈ 0.077 | 4/51 ≈ 0.078 | ~0.006 | ~0.149 |