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Independent Events Probability Calculator

Calculate two-event, multi-event, repeated-trial, exact-k, and complement probabilities under a clearly stated independence assumption.

2 or many eventsAND · OR · none · exactly one · exact k
Repeated trialsbinomial success-count distribution
Flexible inputdecimal · percent · fraction
Assumption auditcompare P(A∩B) with P(A)P(B)
—Both
—At least one
—Exactly one
—Neither
Independent-events probability workbench

Move from two events to exact-k and repeated-trial questions

Calculate common two-event outcomes, combine a custom list of independent probabilities, model repeated identical trials, or check whether a supplied intersection is numerically consistent with independence.

AND · OR · exact k · audit
—All occur
—At least one
—None
—Exactly one
—Selected exact k
—Expected successes

Success-count distribution

SuccessesProbabilityRelative bar

Copyable formula audit

Calculating the sample probabilities…
Independence boundary. These multiplication and success-count models assume events do not change one another's probabilities. Mutually exclusive events with positive probabilities are generally not independent. The intersection-check mode only compares supplied numbers with P(A)×P(B); it cannot prove a real-world causal or sampling process is independent.

Core describe → test → interpret workflows

Use descriptive context first, choose the method that matches the design, then keep inputs, settings and result evidence reproducible instead of copying a p-value without its analysis recipe.

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Interpret statistical results in context

Use plots and descriptive summaries before formal tests. Check the sampling/design assumptions that matter for the chosen method, report effect size and uncertainty where available, and avoid treating a threshold such as p < .05 as proof of importance, causation, or truth. Browser calculations are educational/planning utilities, not domain-specific professional advice.

How to use this Independent Events Probability Calculator

Enter probabilities for events A and B to calculate common independent-event combinations.

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