## Binomial Distribution and Selection of Suitable Values

For binomial distributions, how can you tell which central tendency value (mean, median, or mode) and which variability value (interquartile range, variance, standard deviation, etc.) are most appropriate for the data?

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 Hey Soaring Crane. What exactly are you trying to do? Are you trying to see if a distribution is binomial? Are you assuming its binomial to estimate its parameter? Binomials are good for modelling sums of I.I.D Bernoulli (Yes/No, On/Off etc) type stochastic processes (i.e. random processes).
 Thanks for replying. I am assuming it is binomial (yes/no reply), but I don't know how to determine which values regarding central tendency and variability describe it best. (For example, there are about ten "no" replies and fourteen "yes" replies.)

Recognitions:

## Binomial Distribution and Selection of Suitable Values

 Quote by Soaring Crane which variability value (interquartile range, variance, standard deviation, etc.) are most appropriate for the data?
I think what you are asking is:

Given the data, what is the best method for estimating the parameter of the binomial distribution that fits it?

This is not a precise mathematical question until you define what "best" (or "appropriate") means.

In mathematical terms, you are seeking an "estimator" (i.e. an algorithm or formula whose input is the sample data and whose output is an estimate of the parameter p (the probability of "succsess") that defines a binomial distribution.) An estimator depends on the random values in a sample so the estimator is a random variable. Thus there is no guarantee the estimator will always be close to value you want to estimate. To say what a "best" estimator is, you must be specific about "best" means in scenario that involves random outcomes. Some common ways of expressing human tastes for "best" estimators have the technical names: "maximum liklihood", "unbiased", "minimum variance", "consistent".