I came across a Mike Cohn's blog entry titled How Do Story Points Relate to Hours? where he discusses the relationship between story points and hours. To summarize his thoughts, he states that the bucket for x-story-point estimates represents a distribution of hours. For example, the five-story-point stories estimated by a particular team would relate to a range between 15 and 30 hours. The hours distribution for one-story-point stories is going to be different than the distribution for the eight-story-point stories.
What this also means, is that the distribution in the smaller story point estimates should be much tighter than the distribution in the larger story point estimates. Estimating the size of a story that asks for a new label and a new entry box on a web page should be much easier to estimate than a story that asks for adding single-sign-on support across multiple web applications. To illustrate this concept to my team, I decided to collect data from our last 6 iterations and graph the different story point estimates against both the estimated hours, and the actual hours.
If you were to take a collection of estimated stories from a team and come up with a number like 5 hours per story point, then you would be assuming that all stories are estimated with the same level of accuracy. In practice, the larger stories contain more unknowns and therefore suffer from less accurate estimations than the smaller stories. The graphs above demonstrate this and tell me that each story point level (1,2,3,5, etc) has it's own level of accuracy and using a single conversion for estimation is not a good idea. Other than helping me illustrate the hours relation to my team, how else can this information be used? It can be used to show product owners how important it is for us to break stories down into smaller (hopefully easier estimated) stories.