Binomial Distribution and Selection of Suitable Values

AI Thread Summary
In determining the appropriate central tendency and variability values for a binomial distribution, it is essential to clarify what "best" means in this context. The discussion highlights that estimators, which are algorithms or formulas based on sample data, are used to estimate the parameter p (the probability of success) in a binomial distribution. Common types of estimators include maximum likelihood, unbiased, minimum variance, and consistent estimators. The conversation suggests that if the focus is on the mean and variance rather than p, specific estimators for those parameters should be sought. Understanding the right terminology and context is crucial for obtaining precise mathematical answers.
Soaring Crane
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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?

Thanks for any reply.
 
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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.)
 
Soaring Crane said:
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".

So, to ask a question that has a mathematical answer, you should ask questions like:

"What is a maximum liklihood estimator for the parameter p of a binomial distribution?"

"Is there an estimator of the paramater p of a binomial distribuion that is both unbiased and has minimum variance?"

Some forum member can answer those, or you can find the answers on the web, now that the right jargon is established.

Edit: Or perhaps you don't care about p, but only about the mean and variance of the binomial distribution. In that case you should ask for estimators of those parameters. (Even though the mean and variance are both functions of p, a "best" estimator of p is not necessarily a "best" estimator of the mean or variance.)
 
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