It's a number. Just like the average speed on your sports tracker. You don't estimate it. You know it based on real data. Not some fuzzy ritual.

This is yet another uninformed notion. The *average* is the calculated *central* value of a set of numbers. The *mean *of a series of numbers. This is the *arithmetic *mean of that series. There are other means, but for software estimating the *mean* is the correct term.

However that *mean* is NOT just a number, it's a *calculated *number from the series. And that series has other useful *statistics*. The Variance for example which is the *expectation* of the squared *deviation* of a random variable from its *mean*. Variance measures how far a set of (random) numbers are spread out from their average (mean) value. The variance is the square of the standard deviation, the second central moment of a distribution, and the covariance of the random variable with itself.

Notice here the term *random* variable. All the variables in software development are random variables. That notion in the quote *based on real data* is actually *based on the random variables sampled from the past project's performance*.

The underlying stochastic processes of software development creates *random* variables in accordance with the processes generating the random variable - a *generating function*. These random variables can be collected from past performance OR they can be generated in a simulation process that *models* the underlying stochastic processes of software development.

Monte Carlo Simulation and Method of Moments are processes used to *model* the cost, schedule, and technical performance of projects.

That *real* data mentioned in the quote is *random real data*, so modeling how that random data will impact the future requires several steps

- Are there a sufficient number of samples of past data to produce a statistically credible model of the future. There are several
*tests*for this.? - Is the underlying stochastic process generating these random values
*stationary*- that is will it be the same in the future as it was in the past? - Are the uncertainties that drive the stochastic processes understood, so the proper use of the
*real*data and any*modeled*data be assured? There are tests for this as well.

**The Fallacy of the Week **

The fallacy is that the *number* for the mean is a *random number*, not a cardinal number. That number is subject to change in the future, driven by the underlying uncertainties of the process. So know the *mean* (average) requires you know the Variance (square of the Standard Deviation), the stability of the generating process, and several other *statistics* before that number is of any real use for making decisions.

When you hear what was stated in the quote, it's an indication that the speaker is unaware of the statistical processes that drive the estimating processes, their modeling, and their use in decision making.