Binomial distribution: Difference between revisions

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imported>Doug Williamson
(Add 'limited' before 'whole number' to differentiate from Poisson distribution.)
imported>Doug Williamson
(Amend from 'useful' model to 'appropriate'.)
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The binomial distribution can be a useful model for processes where:
The binomial distribution can be an appropriate model for processes where:


#The process consists of a limited whole number of identical trials or situations (n).
#The process consists of a limited whole number of identical trials or situations (n).

Revision as of 11:19, 7 August 2014

Statistics.

A discrete probability distribution built up from a series of binomial trials.


The binomial distribution can be an appropriate model for processes where:

  1. The process consists of a limited whole number of identical trials or situations (n).
  2. Each trial results in just one of only two possible outcomes (eg success or failure).
  3. The probability of success (p) remains constant for each trial.
  4. The trials are independent, and
  5. Primary interest lies in the probability of a specified number of successes (or of failures) in the n trials.


For example, the total number of sales achieved in a fixed number of sales appointments, assuming the probability of achieving a sale remains constant for each appointment.


See also