Distribution Tables

Hypergeometric Distribution Table

Hypergeometric distribution probability table for sampling without replacement. Essential for quality control and lottery calculations.

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Hypergeometric Distribution Table

The hypergeometric distribution models the probability of k successes in n draws from a finite population of size N containing K successes, without replacement.

Probability Formula

P(X = k) = [C(K,k) × C(N-K, n-k)] / C(N,n)

Where:

  • N = population size
  • K = number of successes in population
  • n = number of draws (sample size)
  • k = number of observed successes
  • C(a,b) = binomial coefficient “a choose b”

Small Population Tables

N = 20, n = 5 (drawing 5 from population of 20)

kK=2K=3K=4K=5K=6K=7K=8K=10
00.55260.39910.28170.19380.12950.08390.05240.0181
10.39470.44200.43020.38740.32980.26870.20980.1150
20.05260.14580.21670.26640.29550.30400.29240.2384
3-0.01320.06300.12460.17950.22600.25870.3117
4--0.00830.02630.05710.09570.13880.2154
5---0.00160.00850.02170.04800.1015

N = 50, n = 10 (drawing 10 from population of 50)

kK=5K=10K=15K=20K=25
00.31060.06360.00950.00100.0001
10.43130.22110.06430.01300.0019
20.20980.29620.16680.05800.0137
30.04420.24040.24630.14440.0517
40.00410.12250.23000.21900.1182
5-0.04380.15700.23070.1787
6-0.01020.07640.17260.1916
7-0.00180.02750.08910.1453
8--0.00580.02890.0727
9--0.00080.00580.0218
10---0.00060.0043

Quality Control Application

Sampling from lot of N=100 items with D defectives

Sample size n = 10

k defectsD=5D=10D=15D=20
00.58380.33050.18210.0951
10.33940.40810.36860.2954
20.07020.20150.29530.3427
30.00650.04770.13120.2114
4-0.01120.03540.0898
5-0.00100.00610.0255

Sample size n = 20

k defectsD=5D=10D=15D=20
00.31930.09510.02430.0054
10.42000.26830.11670.0421
20.20750.29180.21570.1237
30.04820.20670.25250.2171
40.00490.09810.20270.2544
5-0.03190.11710.2073
6-0.00710.04860.1189

Cumulative Probabilities P(X ≤ k)

N = 50, K = 10, n = 10

kP(X = k)P(X ≤ k)P(X ≥ k)
00.06360.06361.0000
10.22110.28470.9364
20.29620.58090.7153
30.24040.82130.4191
40.12250.94380.1787
50.04380.98760.0562
60.01020.99780.0124
70.00180.99960.0022
80.00041.00000.0004

Mean and Variance

ParameterFormula
Mean (μ)n × K/N
Variance (σ²)n × (K/N) × (1 - K/N) × (N-n)/(N-1)
Standard Dev (σ)√Variance

Finite Population Correction

The factor (N-n)/(N-1) is the finite population correction, which reduces variance when sampling a large fraction of the population.


Comparison: Hypergeometric vs Binomial

AspectHypergeometricBinomial
SamplingWithout replacementWith replacement
PopulationFiniteInfinite
ProbabilityChanges each drawConstant
When to usen/N > 0.05n/N < 0.05

Rule of thumb: Use binomial approximation when n < 0.05N (sample is less than 5% of population)


Example Problems

Example 1: Quality Control

Problem: A lot of 50 items contains 10 defectives. If 5 items are sampled, what’s the probability of exactly 2 defectives?

Solution:

  • N = 50, K = 10, n = 5, k = 2
  • P(X = 2) = [C(10,2) × C(40,3)] / C(50,5)
  • P(X = 2) = 0.2098

Example 2: Card Drawing

Problem: In a deck of 52 cards (13 hearts), what’s P(exactly 3 hearts in 5 cards)?

Solution:

  • N = 52, K = 13, n = 5, k = 3
  • P(X = 3) = 0.0815

Example 3: Acceptance Sampling

Problem: A lot of 100 items has 15 defectives. Accept if sample of 10 has ≤ 1 defective. What’s P(accept)?

Solution:

  • P(X ≤ 1) = P(X=0) + P(X=1)
  • P(X ≤ 1) = 0.1821 + 0.3686 = 0.5507

Lottery/Selection Problems

Committee Selection

Problem: 8 men and 6 women available. Select committee of 5. P(exactly 2 women)?

Solution:

  • N = 14, K = 6 (women), n = 5, k = 2
  • P(X = 2) = [C(6,2) × C(8,3)] / C(14,5)
  • P(X = 2) = 0.4196

Lottery Numbers

Problem: 49 numbers, 6 winning. Buy ticket with 6 numbers. P(matching exactly 3)?

Solution:

  • N = 49, K = 6, n = 6, k = 3
  • P(X = 3) = 0.0177 (about 1 in 57)

When to Use Hypergeometric

✓ Sampling WITHOUT replacement
✓ Finite population
✓ Sample size > 5% of population
✓ Quality control sampling
✓ Card/lottery problems
✓ Committee/selection problems


SituationUse
Sample with replacementBinomial
Large population (n/N < 0.05)Binomial approximation
Multiple categoriesMultivariate hypergeometric

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