Jan 16

What is a binomial random variable - analogy with market demand

On the Internet you can find a number of definitions of a binomial random variable, see Wikipedia, Stat Trek or PennState, among others. None of them seems to me as intuitive as the one provided here. The definition is given in three steps.

Step 1. Everybody knows what is a coin: it takes values 1 and 0 with equal probabilities 1/2 and 1/2. With this information, it is easy to understand what is an unfair coin: it takes value 1 with probability p and 0 with probability 1-p, where p is some number between 0 and 1. This definition describes what happens when we toss an unfair coin once.

Step 2. Now let us toss the coin n times and count the number of successes (getting 1 means success and getting 0 means failure). The random variable that describes the number of successes is called a binomial variable. There is little you can do with this definition; we need to make one more step. Let us denote B_n the binomial variable and let C_1, ..., C_n be the outcomes on the coins. The fundamental fact suggested by the procedure of counting the number of successes is that B_n=C_1+...+C_n.

To illustrate this equation, consider the case n=2. There are 4 possible combinations of the outcomes on the two coins: 1) C_1=0,\ C_2=0, 2) C_1=0,\ C_2=1, 3) C_1=1,\ C_2=0 and 4) C_1=1,\ C_2=1. Plug the coin values in the equation B_2=C_1+C_2, and you will see that in each case the equation is true.

Regarding our experiment of tossing the coin n times, two remarks are in order: 1) obviously, the outcomes are independent and 2) the coins are identically distributed in the sense that the probability p does not change throughout the experiment.

Step 3. It is possible to give different (and equivalent) definitions for the same thing. The one that takes the bull by the horns and can be directly applied is called a working definition. For the binomial random variable, the working definition is this: it is a sum of independent identically distributed unfair coins. That is, you write B_n=C_1+...+C_n and then specify that the coins C_1,...,C_n are independent and have the same p.

Every Economics student knows that the market demand is equal to the sum of individual demands: D_{market}=D_1+...+D_n. The definition of the binomial variable is a perfect analog of this fact. Sums of random variables are omnipresent in Statistics and Theory of Probabilities. By omitting the working definition of the binomial variable, elementary Statistics textbooks, including AP Stats and Business Stats, miss the essence of Statistics.

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