Quick guide
How to use this calculator
- Enter the observations, probabilities, model parameters, or summary statistics requested by the visible labels.
- Keep every value on the same scale and confirm that the selected sampling relationship, distribution, and tail convention match the question you are investigating.
- Read the result together with its assumptions and interpretation. Statistical output summarizes uncertainty under a model; it does not repair biased data or establish causation.
Calculation method
How the poisson binomial distribution calculator works
The Poisson-binomial mass is the coefficient of z^k in ∏[(1−pᵢ)+pᵢz].
The model preserves unequal trial probabilities instead of replacing them with a single average probability.
Worked example
Poisson Binomial Distribution example
Probabilities 0.2, 0.5, and 0.8 give P(exactly 2 successes)=0.42.
The Poisson-binomial mass is the coefficient of z^k in ∏[(1−pᵢ)+pᵢz].
Supported inputs
Precision and limits
Model and design
Trials must be independent and each listed probability must correspond to one Bernoulli trial; dependence requires a different model.
Numerical scope
Inputs use double-precision numerical methods with guarded domains. Datasets accept up to 10,000 finite plain-decimal values. Extremely large parameters or probabilities deep in a numerical tail may require specialist statistical software.
Interpretation
The model preserves unequal trial probabilities instead of replacing them with a single average probability.
Decision boundary
The calculator does not validate how data were collected, diagnose dependence or bias, choose a scientifically meaningful effect, or replace review by a qualified statistician for consequential research, medical, regulatory, safety, or policy decisions.
Privacy
Entered values and calculated results stay in this browser and are not sent to an analytics service.
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