PMF, PDF, and CDF
A concise reference for interpreting probability mass, probability density, and cumulative probability functions.
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Open Interactive Interval ProbabilityPMF: Exact Probabilities For Discrete Variables
The PMF gives probabilities for exact values, such as P(X = 3).
It applies to count data like binomial and Poisson outcomes.
PDF: Density For Continuous Variables
For continuous models, the PDF is not P(X = x). Probability comes from area under the curve over intervals.
That is why P(X = exact value) is 0 in continuous settings.
CDF: Cumulative Probability
The CDF is P(X ≤ x). It maps x-values to probabilities from 0 to 1.
Inverse CDF operations give quantiles and percentiles.
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