Probability Distribution Calculator

PDF, CDF & statistics

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About Probability Distribution Calculator

A probability distribution calculator supporting normal, binomial, Poisson, and exponential distributions. Compute PDF/PMF values, cumulative probabilities (CDF), mean, variance, standard deviation, and quantiles. All calculations are client-side. Essential for statistics, data science, and quality control.

Probability Distribution Calculator Features

  • 4 distributions
  • PDF/CDF
  • Mean/variance
  • Quantiles
  • Parameters
Probability distributions: Normal N(μ,σ²) — bell curve, Binomial B(n,p) — n trials, Poisson P(λ) — rare events, Exponential Exp(λ) — wait times. PDF gives probability density; CDF gives P(X≤x). Mean = E[X], Variance = E[(X−μ)²].

How to Use

Select a distribution:

  • Parameters: μ, σ, n, p, λ
  • Evaluate: P(X=x), P(X≤x)
  • Output: PDF/CDF + statistics

Distributions

  • Normal: continuous, symmetric bell
  • Binomial: discrete, n trials with p
  • Poisson: discrete, rate λ events
  • Exponential: continuous, memoryless

Which Distribution?

Fixed trials → Binomial. Count per interval → Poisson. Time between events → Exponential. Sum of many variables → Normal (CLT).

Step-by-Step Instructions

  1. 1Select distribution type.
  2. 2Enter parameters.
  3. 3Enter x value.
  4. 4View PDF and CDF.
  5. 5Check mean and variance.

Probability Distribution Calculator — Frequently Asked Questions

What's the difference between PDF and CDF?+

PDF (probability density function) gives the relative likelihood at point x. CDF (cumulative distribution function) gives P(X ≤ x). For discrete distributions, PDF is called PMF (probability mass function).

When should I use the Poisson distribution?+

When counting events in a fixed interval (time, area, volume) where events are independent and occur at a constant average rate λ. Examples: emails per hour, defects per unit, accidents per year.

What is the Central Limit Theorem?+

The sum/mean of many independent random variables approaches a normal distribution regardless of the original distribution. This is why normal distributions appear everywhere in nature and statistics.

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