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Normal Approximation Calculator

Free normal approximation calculator. Approximate binomial, Poisson, hypergeometric with normal. Exa

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Why This Statistical Analysis Matters

Why: Statistical calculator for analysis.

How: Enter inputs and compute results.

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STATISTICSInference & Tests

Normal Approximation — When Discrete Meets Continuous

Approximate binomial, Poisson, and hypergeometric distributions with the normal curve. Compare exact vs approximate probabilities (with and without continuity correction) and see relative error.

Real-World Scenarios — Click to Load

Distribution Type

Probability Type

Inputs

normal_approx.sh
CALCULATED
$ normal_approx --dist="binomial" --prob="exact"
Exact
7.9589%
Approx (no CC)
7.9788%
Approx (with CC)
7.9656%
Rel. Error (no CC)
0.25%
Rel. Error (CC)
0.08%
μ = 50.0000, σ = 5.0000 ✓ Valid approx
P(X=50)Φ(50+0.550.005.0000)Φ(500.550.005.0000)P(X = 50) \approx \Phi\left(\frac{50+0.5-50.00}{5.0000}\right) - \Phi\left(\frac{50-0.5-50.00}{5.0000}\right)
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Normal Approximation Result
Binomial — exact
Exact: 7.96%
Approx (CC): 7.97%Rel. Error: 0.08%Valid
numbervibe.com/calculators/statistics/normal-approximation-calculator

Exact PMF vs Normal Curve Overlay

Exact vs Approximate Probability Comparison

Relative Error: No CC vs With CC

Calculation Breakdown

PARAMETERS
Mean μ
50.0000
μ = np = 100 × 0.5
Std Dev σ
5.0000
σ = √(np(1-p)) = √(100×0.5×0.50)
VALIDITY
Validity
✓ np≥5, n(1-p)≥5
PROBABILITIES
Exact P(X=k)
7.9589%
ext{Binomial} ext{PMF}
Approx (with CC)
7.9656%
Φ((k+0.5-\text{mu} )/\text{sigma} ) - Φ((k-0.5-\text{mu} )/\text{sigma} )
ACCURACY
Rel. Error (no CC)
0.25%
Rel. Error (with CC)
0.08%

For educational and informational purposes only. Verify with a qualified professional.

Key Takeaways

  • • Binomial X ~ Bin(n,p) ≈ N(np, np(1-p)) when np ≥ 5 and n(1-p) ≥ 5
  • • Poisson X ~ Poi(λ) ≈ N(λ, λ) when λ ≥ 10 (or λ ≥ 5 for rough approximation)
  • • Hypergeometric ≈ Normal with μ = nK/N, σ² = nK(N-K)(N-n)/(N²(N-1))
  • • Continuity correction: P(X=k) uses P(k-0.5 ≤ X ≤ k+0.5); P(X≤k) uses k+0.5; P(X≥k) uses k-0.5
  • • Always compare exact vs approximate and report relative error

Did You Know?

📐The continuity correction adds ±0.5 because discrete bars occupy unit intervals — the bar at k spans [k-0.5, k+0.5]Source: Wikipedia
🎲For n=100, p=0.5, the binomial is nearly symmetric and the normal approximation is excellentSource: Khan Academy
🦠Poisson with λ=10+ is often approximated by N(λ,λ) — same mean and varianceSource: NIST Handbook
📦Hypergeometric variance is smaller than binomial due to finite population correction (N-n)/(N-1)Source: OpenIntro
📊Quality control uses normal approximation to quickly assess defect rates without summing many binomial termsSource: Industry practice
💊Clinical trials with large n use normal approximation for hypothesis tests on proportionsSource: FDA guidance

How It Works

1. Binomial → Normal

μ = np, σ = √(np(1-p)). Valid when np ≥ 5 and n(1-p) ≥ 5. Use z = (k ± 0.5 - μ)/σ with continuity correction.

2. Poisson → Normal

μ = λ, σ = √λ. Valid when λ ≥ 10. P(X≤k) ≈ Φ((k+0.5-λ)/√λ); P(X≥k) ≈ 1 - Φ((k-0.5-λ)/√λ).

3. Hypergeometric → Normal

μ = nK/N, σ² = nK(N-K)(N-n)/(N²(N-1)). Finite population correction reduces variance compared to binomial.

4. Continuity Correction Rules

P(X=k): use Φ((k+0.5-μ)/σ) − Φ((k-0.5-μ)/σ). P(X≤k): use Φ((k+0.5-μ)/σ). P(X≥k): use 1 − Φ((k-0.5-μ)/σ).

5. Relative Error

Relative error % = |approximate − exact| / exact × 100. Continuity correction typically reduces error.

Expert Tips

When to Use Normal Approx

Binomial: np ≥ 5 and n(1-p) ≥ 5. Poisson: λ ≥ 10. Check validity before trusting results.

Always Use CC

Continuity correction improves accuracy, especially for P(X=k) and small k. Without CC, errors can exceed 10%.

Exact vs Approx

When in doubt, compute exact. Use approximation for quick estimates or when n is very large.

Hypergeometric Caveat

Sampling without replacement — variance is smaller. Use when n/N is not negligible.

Comparison: Exact vs Approximate

MethodP(X=k)P(X≤k)P(X≥k)
ExactPMFCDF1-CDF(k-1)
Normal (no CC)PDF×1 (poor)Φ((k-μ)/σ)1-Φ((k-μ)/σ)
Normal (with CC)Φ((k+0.5-μ)/σ)−Φ((k-0.5-μ)/σ)Φ((k+0.5-μ)/σ)1-Φ((k-0.5-μ)/σ)

Frequently Asked Questions

When is the normal approximation valid for binomial?

When np ≥ 5 and n(1-p) ≥ 5. Both conditions must hold for a good approximation.

What is continuity correction?

Discrete distributions have probability mass at integers. The normal is continuous. Adding ±0.5 aligns boundaries: P(X≤k) uses k+0.5 so the area under the normal from -∞ to k+0.5 approximates the discrete CDF at k.

Why does Poisson have mean = variance?

The Poisson distribution has E(X)=λ and Var(X)=λ. So the normal approximation uses N(λ, λ) — same mean and variance.

When to use hypergeometric vs binomial?

Binomial: sampling with replacement or infinite population. Hypergeometric: sampling without replacement from finite N. If n/N is small, binomial approximates hypergeometric.

Which is better: with or without continuity correction?

With continuity correction is almost always more accurate, especially for P(X=k) and tail probabilities.

Validity Rules by the Numbers

np ≥ 5
Binomial (both)
λ ≥ 10
Poisson rule
±0.5
Continuity CC
Φ(z)
Standard normal

Disclaimer: Normal approximation is an approximation. When validity conditions fail (e.g., np < 5), use exact methods. For critical applications, verify with established statistical software.

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