Data Distributions

When presented with thousands of pieces of information, you can break the numbers down into individual values (or ranges of values) and provides the number of individual data items that take on each value or range of values. This is called a frequency distribution. If the data can only take on specific values, as is the case when we record the number of goals scored in a soccer game, it is called a discrete distribution. When the data can take on any value within the range, as is the case with income or market capitalization, it is called a continuous distribution.

The advantage of a presenting the data in a distribution is two fold. One is that you can summarize even the largest data sets into one distribution and get a measure of what values occur most frequently and the range of high and low values. The second is that the distribution can resemble one of the many common distributions about which we know a great deal in statistics. Consider, for instance, the distribution that we tend to draw on the most in analysis: the normal distribution, illustrated in figure 1.

Figure 1: Normal Distribution

A normal distribution is symmetric, has a peak centered around the middle of the distribution and tails that are no fat and stretch to include infinite positive or negative values. Figure 2 illustrates positively and negatively skewed distributions.

Figure 2: Skewed Distributions

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