Briefly, normal distributions are often arise as the histogram of a set of measurements. They have some central value (called the mean) and some dispersion (called standard deviation) around the mean. Anyone who took a stat class studied these distributions. They show up in a many contexts:

- The distribution resulting from tabulating the histogram of repeated, but imprecise measures of some quantity and then divided the entries by the sum of the measures is often assumed to be normal. The mean of the distribution is the estimator of the actual measure.

- Measurements of natural sets of populations are assumed to be normal. For example IQ tests and their scoring are designed to have mean 100 and standard deviation 15. Note actual measures cannot be assumed to be normal. For example, the height of American males is sort of normal but skewed to lower heights.

The likelihood of a value falling in a range is given by the area under the curve. For example, the probability of a value of the normally distributed random variable falling within one standard deviation of the mean is 68.2%.

Normal distributions have one really cool feature called the Central Limit Theorem, which states that under remarkably general conditions, the sum of a set of random variables will be close to normal. Notice, in the previous blog entry, when we added two triangular random variables, the sum appeared smooth and in fact started to look normal.

All that said, I do have have a pet peeve. Normal distributions are overused. Most things in nature and economics are not normally distributed. For example, as as documented in Wikipedia, these phenomena are nowhere near normal, but are closer to a Pareto distribution:

- The sizes of human settlements (few cities, many hamlets/villages)
- File size distribution of Internet traffic which uses the TCP protocol (many smaller files, few larger ones)
- Hard disk drive error rates
- The values of oil reserves in oil fields (a few large fields, many small fields)
- The length distribution in jobs assigned supercomputers (a few large ones, many small ones)[citation needed]
- The standardized price returns on individual stocks
- Fitted cumulative Pareto distribution to maximum one-day rainfalls
- Sizes of sand particles
- Sizes of meteorites
- Areas burnt in forest fires
- Severity of large casualty losses for certain lines of business such as general liability, commercial auto, and workers compensation.

Getting back to our topic, let's turn to triangular distributions. They are not used to describe a set of measured outcomes from an experiment.

**They are used to describe what we know or believe about some unknown random variable.**For example, the sales of a new product one year after delivery generally can not be determined by measuring the sales of a bunch of new products. As pointed out by Douglas Hubbard, treating the future sales as a single fixed variable is unreasonable (although all too common). What is more reasonable is setting the low (L), high (H) , and most likely (E) values of the future sales. As I wrote in an earlier entry, these are the values that specify a triangular distribution. I.e. triangular distributions are set to zero below a given low value, L, and above the high value, H, and peaks at the expected value E. The distribution is then a describe be a triangular curve so that the total area is 1. Here is the distribution for L = 1, E=6, and H=7.

To wrap up, normal distributions are occasionally useful to describe outcomes of measurements while triangular distributions are useful for giving rough estimates of one's belief of the liklihood of outcomes based on the evidence on hand. More generally, normal distributions are useful in

*frequentist*statistics and triangular in

*Bayesian*statistics. See this Wikepedia article for a discussion of the kinds of statistics. Much of what we do in development analytics is more Bayesian than frequentist. I hope to write more about that in the near future.