Webwhompers

The long tail (above) looks exactly like a power law distribution (below):

We have used the power law distribution previously to describe the effects of cumulative advantage, or "rich get richer." However, the long tail describes something utterly different than "rich get richer." Consider that megahits are the big winners in the "rich get richer" world described by Watts, but they are the dying paradigm of the "long tail" world described by Anderson.

How can the power law curve describe both the preeminence of megahits and their imminent demise?

To answer this question, it helps to examine more carefully the long tail curve and how it differs from the traditional power law distribution. Below we draw a generic long tail curve with units:

We see two important distinctions between the long tail curve and the power law distribution:

1. Megahits and niche items are featured in both the long tail curve and the power law distribution, but they swap places depending on which curve is drawn. The long tail curve is, in a sense, a flipped-around version of the power law distribution.
2. The x- and y-units of the long tail curve are different than the x- and y-units of the power law distribution. In particular
   
   • The y-units of the long tail curve are rather similar to the x-units of a degree distribution (power law or otherwise): sales per unit time and node degree both indicate popularity.
   • The x-axis of the long tail curve depends on sorting items by popularity. The result is akin to sorting people in a line by height and observing the curve described by the tops of their heads. This is not exactly a probability distribution; it is based on a different construction.