In-class demo: The binomial distribution and the bell curve

Many years ago, the only available in-class technology at my university was the Microsoft Office suite — probably Office 95 or 98. This placed severe restrictions on what I could demonstrate in my statistics class, especially when I wanted to have an interactive demonstration of how the binomial distribution gets closer and closer to the bell curve as the number of trials increases (as long as both np and n(1-p) are also decently large).

The spreadsheet in the link below is what I developed. It shows

  • The probability histogram of the binomial distribution for n \le 150
  • The bell curve with mean \mu = np and standard deviation \sigma = \sqrt{np(1-p)}
  • Also, the minimum and maximum values on the x-axis can be adjusted. For example, if n = 100 and p = 0.01, it doesn’t make much sense to show the full histogram; it suffices to have a maximum value around 5 or so.

In class, I take about 3-5 minutes to demonstrate the following ideas with the spreadsheet:

  • If n is large and both np and n(1-p) are greater than 10, then the normal curve provides a decent approximation to the binomial distribution.
  • The probability distribution provides exact answers to probability questions, while the normal curve provides approximate answers.
  • If n is small, then the normal approximation isn’t very good.
  • If n is large but p is small, then the normal approximation isn’t very good. I’ll say in words that there is a decent approximation under this limit, namely the Poisson distribution, but (for a class in statistics) I won’t say much more than that.

Doubtlessly, there are equally good pedagogical tools for this purpose. However, at the time I was limited to Microsoft products, and it took me untold hours to figure out how to get Excel to draw the probability histogram. So I continue to use this spreadsheet in my classes to demonstrate to students this application of the Central Limit Theorem.

Excel spreadhseet: binomial.xlsx

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