Binomial Distribution Calculator — PMF, CDF, and Inverse
Compute binomial probabilities (PMF/CDF) and quantiles with GetCalcMaster’s stats functions. Includes worked examples (educational).
The binomial distribution models the number of successes in n independent trials with success probability p. Use this guide for PMF (exact probability), CDF (cumulative probability), and inverse CDF (quantiles).
What this calculator is
The Statistics Calculator is an interactive tool inside GetCalcMaster. It’s designed to help you explore scenarios, understand formulas, and document assumptions.
Key features
- Immediate results as you change inputs
- Transparent assumptions and explainable outputs
- Works well with the built‑in Notebook for saving scenarios
Formula
PMF: P(X=k) = C(n,k) p^k (1−p)^(n−k)
CDF: P(X ≤ k)
Quantile: smallest k with CDF(k) ≥ qQuick examples
binompmf(7, 20, 0.4) # ≈ 0.165882binomcdf(7, 20, 0.4) # ≈ 0.415893binominv(0.95, 20, 0.4)# Sanity check: CDF should increase with k binomcdf(8, 20, 0.4) - binomcdf(7, 20, 0.4)
Verification tips
- CDF should be non-decreasing as k increases.
- PMF values should sum to ~1 over k=0..n (within rounding).
- Use consistent definitions for a ‘trial’ and ensure independence is reasonable.
Common mistakes
- Mixing up k and n (k is successes, n is total trials).
- Using p as a percent (40 instead of 0.40).
- Assuming independence when trials are dependent (can break the model).
How to use it (quick steps)
- Set n (number of trials) and p (success probability).
- Decide whether you need PMF, CDF, or inverse CDF.
- Open the Statistics Calculator and use binompmf(k, n, p), binomcdf(k, n, p), or binominv(q, n, p).
- Verify that probabilities are between 0 and 1, and that CDF is non-decreasing in k.
- Document the trial definition and the independence assumption.
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FAQ
What does binompmf(k,n,p) compute?
What does binomcdf(k,n,p) compute?
What does binominv(q,n,p) return?
Tip: For reproducible work, save your inputs and reasoning in Notebook.