Probability Calculator

Probability calculations underpin decisions in statistics, data science, risk analysis, and everyday reasoning, yet the relationships between union, intersection, and conditional probability are frequently confused. Many people intuitively add the probabilities of two events to find the chance that either occurs, forgetting to subtract the overlap. This leads to answers greater than 1 — a mathematical impossibility that reveals the error immediately. The inclusion-exclusion principle, which governs P(A union B), is one of the most practically important formulas in probability and the one this tool computes automatically.

Conditional probability adds another layer by answering a different question entirely: given that event B has occurred, what is the revised probability of A? This is the foundation of Bayesian reasoning, medical test interpretation, and fault tree analysis. When a doctor tells you a test is 95 percent accurate and your result is positive, you need P(disease | positive test), not P(positive | disease) — these are different quantities and the distinction has life-altering implications. This calculator makes both the basic set operations and the conditional probability visible in a single interface.

| Quantity | Formula | Independent Events | Mutually Exclusive |

| --- | --- | --- | --- |

| P(A union B) | P(A) + P(B) - P(A intersect B) | P(A) + P(B) - P(A)*P(B) | P(A) + P(B) |

| P(A intersect B) | Given or computed | P(A) * P(B) | 0 |

| P(A complement) | 1 - P(A) | Same | Same |

| P(A | B) | P(A intersect B) / P(B) | P(A) | 0 |

Enter P(A) and P(B) as decimal values between 0 and 1 — for example, 0.5 represents 50 percent probability.

Either enter P(A intersect B) directly, or check the 'independent' checkbox to have it computed as P(A) multiplied by P(B).

View all computed results instantly: P(A union B), P(A complement), P(B complement), and conditional probability P(A | B).

Adjust any input and watch every output recalculate in real time.

Verify your results with simple known cases. If A and B are mutually exclusive (set P(A intersect B) to 0), then P(A union B) should equal P(A) plus P(B) exactly. If A and B are independent with P(A) = 0.5 and P(B) = 0.4, then P(A intersect B) should be 0.2 and P(A union B) should be 0.7. Check that all probabilities fall between 0 and 1 inclusive, and that P(A | B) equals P(A) when the events are independent — this is the defining property of independence.

Adding P(A) and P(B) without subtracting P(A intersect B), which overestimates the union probability.

Confusing P(A | B) with P(B | A) — these are not equal unless A and B are independent.

Checking 'independent' when the events are actually dependent, which produces incorrect intersection and conditional values.

Entering P(A intersect B) larger than either P(A) or P(B), which is mathematically impossible and indicates an input error.

When P(B) is zero, the conditional probability P(A | B) is undefined because division by zero has no meaning — the tool handles this gracefully. When P(A intersect B) equals zero, the events are mutually exclusive and the union simplifies to a simple sum. The tool accepts probabilities as decimals, so a 73 percent chance is entered as 0.73. For events that are neither independent nor mutually exclusive — which is the most common real-world scenario — you must know or estimate the intersection probability from data rather than computing it from the independence assumption.

Calculating the probability that at least one of two delivery trucks arrives on time when you know each truck's individual reliability and their joint on-time rate.

Interpreting medical screening results by computing P(disease | positive test) using known sensitivity, specificity, and disease prevalence.

Assessing project risk by finding the probability that either of two independent failure modes occurs during a mission-critical deployment.

Verifying homework solutions for statistics courses by cross-checking inclusion-exclusion calculations.

Q: What is P(A union B)?

A: The probability that A or B (or both) occur: P(A union B) = P(A) + P(B) - P(A intersect B).

Q: What is P(A intersect B)?

A: The probability that both A and B occur. For independent events, it equals P(A) times P(B). For mutually exclusive events, it is 0.

Q: What is conditional probability?

A: P(A | B) = P(A intersect B) / P(B) — the probability of A given that B has already occurred.

Q: How do I model mutually exclusive events?

A: Enter P(A intersect B) = 0 (or uncheck independent and set the field to 0). The union then simplifies to P(A) + P(B).

Q: What happens if I check independent but the events are not?

A: The tool will compute P(A intersect B) as P(A) times P(B), which will be incorrect. Only use the independent checkbox when you are certain the events do not influence each other.

Try the Probability Calculator tool. See also Permutation & Combination Calculator, Z-Score Calculator, and Average Calculator.