Continuous & Normal Distributions in Business: Uses & Examples

Instructor: Scott Tuning

Scott has been a faculty member in higher education for over 10 years. He holds an MBA in Management, an MA in counseling, and an M.Div. in Academic Biblical Studies.

When an organization seeks to analyze data for the purpose of taking action, the data must meet certain criteria. This lesson explores one of these criteria called a normal distribution.

Assessing Your Market

How would the people around you react if you asked them a question like, ''Do you think that we should expel all illegal aliens from the U.S. by 2025?'' You're likely to get quite a few answers depending on where you are and who you're talking with.

If you ask some UC Berkley students this question, you're likely to get answers that are very different than the ones from a group of 40-something, white males at the annual meeting of the National Rifle Association.

Businesses (and politicians) need accurate data about the market in which they operate so that they can meet the needs of their customers. Analyzing the distribution of that data helps ensure that business decisions are not made based on a group that doesn't actually represent the larger group as a whole.

When researchers choose a group to study, the selected group is called a sample and the larger group that the sample should represent is called a population. In the example, the college students and the NRA members are both samples and everyone in the United States is the population.

Continuous Distribution

For the major commercial airlines, fuel is often the most significant cost of doing business. Most airlines speculate and purchase fuel months in advance, hoping to secure as much fuel as possible at the lowest price possible.

The price of fuel is an example of a continuous distribution of data because it is never a fixed value. Analyzing continuous distributions, airlines look at industry trends over time in order to identify the best times to buy fuel. Using a continuous distribution in a statistical algorithm is a means of reducing the risk of price volatility.

The fact that fuel prices are constantly changing makes the price of fuel a continuous distribution.

The continuous distribution is essentially the price of fuel at any given moment in time. It is continuous because the 'supply' of fuel prices is never truly exhausted. However, if we said that there were 1,000 gallons of fuel in a tank that sells for $5.00 per gallon, the 'gallon' becomes discrete, since there are only 1,000 gallons of fuel. If airline A buys 200 gallons, there are only 800 gallons left.

Normal Distribution

Now, it's very important for businesses to make sure their data is normally distributed, in other words, verified to represent the population that is being studied.

Between 2000 and 2009, the price of jet fuel rose more than 260%, but toward the end of the decade, the price of jet fuel plunged as crude oil prices plummeted. If you were a financial executive at a commercial airline, it would be important to understand the impact of this short-term drop across the long-term backdrop.

If you were create a running average of jet fuel prices year-over-year, it might look something like the following.


A plot of jet fuel prices over time.
continuous distribution


You can see that the trend-line remains accurate despite the 2008-2009 price drop.

Imagine how this would change without the longer trend displayed. If an airline projected the upcoming year's costs based only on the year before, there would be years in which the airline lost tens of millions of dollars. These loses would be the result of calculations computing a simple average using a very limited data set that did not accurately characterize the price patterns.

An occurrence like this is a poignant example of why ensuring normal distributions is so important when making evidence-based decisions. The most common way to visually depict data that is normally distributed is using what is commonly referred to as the bell curve.

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