Three-point estimation

The three-point estimation technique is used in management and <a href="/facts/Information_systems/apcSzFLh">information systems</a> applications for the construction of an approximate <a href="/facts/Probability_distribution/EpsKKVRu">probability distribution</a> representing the outcome of future events, based on very limited information. While the distribution used for the approximation might be a <a href="/facts/Normal_distribution/UapjjPyQ">normal distribution</a>, this is not always so. For example, a <a href="/facts/Triangular_distribution/fs5XhVFP">triangular distribution</a> might be used, depending on the application.
In three-point estimation, three figures are produced initially for every distribution that is required, based on prior experience or best-guesses:

<ul><li>a = the best-case estimate</li>
<li>m = the most likely estimate</li>
<li>b = the worst-case estimate</li></ul>
These are then combined to yield either a full probability distribution, for later combination with distributions obtained similarly for other variables, or summary descriptors of the distribution, such as the <a href="/facts/Mean/swcEd4Pg">mean</a>, <a href="/facts/Standard_deviation/qSug9BRl">standard deviation</a> or <a href="/facts/Percentile/YKD2SDsw">percentage points</a> of the distribution. The accuracy attributed to the results derived can be no better than the accuracy inherent in the three initial points, and there are clear dangers in using an assumed form for an underlying distribution that itself has little basis.

Three-point estimation open-in-new

Three-point estimation