Statistics

Finally, think of a histogram in which the vertical bars are very narrow...and then very, very narrow. As one connects the midpoints of these bars, the frequency polygon begins to look like a smooth curve, perhaps like a high, smoothly shaped hill. A curve of this kind is known as a distribution curve.

Probably the most familiar kind of distribution curve is one with a peak in the middle of the graph that falls off equally on both sides of the peak. This kind of distribution curve is known as a "normal" curve. Normal curves result from a number of random events that occur in the world. For example, suppose you were to flip a penny a thousand times and count how many times heads and how many times tails came up. What you would find would be a normal distribution curve, with a peak at equal heads and tails. That means that, if you were to flip a penny many times, you would most commonly expect equal numbers of heads and tails. But the likelihood of some other distribution of heads and tails—such as 10% heads and 90% tails—would occur much less often.

Frequency distributions that are not normal are said to be skewed. In a skewed distribution curve, the number of cases on one side of the maximum is much smaller than the number of cases on the other side of the maximum. The graph might start out at zero and rise very sharply to its maximum point and then drop down again on a very gradual slope to zero on the other side. Depending on where the gradual slope is, the graph is said to be skewed to the left or to the right.