The Excel NEGBINOM.DIST function returns the negative binomial distribution — the probability of seeing a given number of failures before a target number of successes occurs, when each trial has a fixed success probability.
Syntax
| Argument | Description | |
|---|---|---|
number_f | Required | The number of failures. |
number_s | Required | The threshold number of successes. |
probability_s | Required | The probability of a success on each trial (between 0 and 1). |
cumulative | Required | TRUE returns the cumulative distribution (probability of at most number_f failures); FALSE returns the probability mass at exactly number_f failures. |
How to use it
NEGBINOM.DIST answers questions like “what's the chance of exactly 10 failures before my 5th success, when each trial succeeds 25% of the time?”
Set cumulative to FALSE for the probability of exactly that many failures (the mass function), or TRUE for the running total up to and including that count. The arguments are checked: a negative count, or a probability outside 0–1, returns an error.
Failures, then a success threshold. The first argument is the number of failures; the second is how many successes you're waiting for. Mixing up the order is the most common mistake.
Try it: interactive demo
Pick a NEGBINOM.DIST example to see the formula and its result.
Practice workbook
Frequently asked questions
What does the negative binomial distribution model?
What is the difference between cumulative TRUE and FALSE?
FALSE gives the probability of exactly number_f failures (the probability mass). TRUE gives the cumulative probability of that many or fewer failures.How is NEGBINOM.DIST different from the old NEGBINOMDIST?
NEGBINOM.DIST (Excel 2010+) adds the cumulative argument, so it can return either the mass or the cumulative value. The legacy NEGBINOMDIST only returned the probability mass.What order do the arguments go in?
NEGBINOM.DIST(number_f, number_s, probability_s, cumulative). Swapping failures and successes is the most common error.Master functions like this in one day
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