BINOM.DIST Function (LibreOffice Calc)

• Computes P(X = k) (PMF)
• Computes P(X ≤ k) (CDF)
• Models success/failure processes
• Used in quality control, reliability, risk modeling, and statistics

BINOM.DIST(number_s; trials; probability_s; cumulative)

Arguments

• number_s:
  Number of successes (integer ≥ 0).

• trials:
  Number of trials (integer ≥ 0).

• probability_s:
  Probability of success on each trial (0–1).

• cumulative:
  TRUE → cumulative distribution (CDF)
  FALSE → probability mass function (PMF)

PMF: Probability of exactly 3 successes

=BINOM.DIST(3; 10; 0.5; FALSE)

CDF: Probability of 3 or fewer successes

=BINOM.DIST(3; 10; 0.5; TRUE)

Using cell references

=BINOM.DIST(A1; B1; C1; D1)

Quality control: probability of ≤ 2 defects

=BINOM.DIST(2; 100; 0.01; TRUE)

Reliability: probability of exactly k failures

=BINOM.DIST(k; n; p; FALSE)

Compute probability of a range (a ≤ X ≤ b)

=BINOM.DIST(b; n; p; TRUE) - BINOM.DIST(a-1; n; p; TRUE)

Compute expected value and compare to PMF

=n * p

Compute tail probability (X > k)

=1 - BINOM.DIST(k; n; p; TRUE)

Use with COMBIN for manual PMF calculation

=COMBIN(n; k) * p^k * (1-p)^(n-k)

BINOM.DIST returns a numeric probability between 0 and 1

Accepts:

• Integer number_s
• Integer trials
• 0 ≤ probability_s ≤ 1

Behavior details

• If number_s > trials → returns 0
• If probability_s = 0 → PMF returns 1 only when number_s = 0
• If probability_s = 1 → PMF returns 1 only when number_s = trials
• CDF is always non‑decreasing

Invalid input → Err:502

BINOM.DIST of an error → error propagates

• Use PMF (FALSE) for exact probabilities
• Use CDF (TRUE) for cumulative probabilities
• Validate integer inputs
• Use BINOM.DIST.RANGE for multi‑value ranges
• Use BINOM.INV for quantile calculations

• Use BINOM.DIST.RANGE for multi‑value ranges
• Use BINOM.INV for inverse binomial distribution
• Use POISSON.DIST for rare‑event approximations
• Use COMBIN for manual PMF calculations

By mastering BINOM.DIST, you can build precise, reliable statistical and probability‑driven models in LibreOffice Calc.