There's a popular myth that says project process can't forecast the future. This is literally not a correct statement, since this phrase leaves out the confidence with which the future is being forecast.

Add to the counter arguments used many times about Black Swans (a book of the same name is either loathed or loved depending on you point of view). But Black Swans are not usually in the vocabulary of project management. We as project managers aren't managing portfolios of investment funds, forecasting earth quakes, or making other decisions in that.

We're taking Statements of Work, Requirements, Concepts of Operations, and turning them into plans and cost models. Emphasis here on "model."

So the real question is

*How confident are we that our model is credible?**Do we know anything about the underlying statistical behavior of this model?**Can we ask probabilistic questions of the model?*

By credible I mean, there is a confidence level that the model can be used to guide the work efforts. The answer to knowing the level of confidence starts with knowing something about the variance in the underlying random variables that make up the model. Pat Weaver talks about Dancing with Chance. Like all of Pat's posts, there is an associated paper with more details. Pat's site is a "Must Subscribe" in Google Reader.

But here's an issue with the word "Chance." Change is a probability term. The local weather forecaster uses the phrase "there is a 30% chance of snow tomorrow on the Front Range." This means there is a 30% probability to snow will occur over the forecast area. This does not mean there is a 30% chance it will snow at our house, since local weather conditions drive storms coming from the north (Wyoming) to dump huge amounts of snow here before reaching other parts of the Front Range.

To know if it is going to snow heavily in Niwot, Colorado, we need to know both the probabilities and the underlying statistics of the behavior of the storm. In the same way we need to know the underlying statistics of the cost and schedule processes to know the probabilities of completing "on or before" for a "cost or less."

**Probability Versus Statistics**

In scheduling or cost when we speak of probabilistic outcomes -* what's the probability of being on time and on budget* - we also need to speak of the statistical nature of the activity network that models the durations and associated costs.

When we speak of a probabilistic activity network (a Bayesian network) we also need to speak in terms of probability.

A question that can be asked of the network is – “ what is the probability of completing this task by a certain date?”

But first we must asked – “what are the underlying statistics of the activities of the network?” Without this knowledge we cannot build a model (or at least a credible model) of the project's cost and schedule.

A final question that needs to be asked is “what is the inherent uncertainty in these estimates?” In other words – how good is our ability to guess in the presence of a statistical process?

So to have a useful model of the schedule and cost and to avoid falling into the trap Pat suggests - having the illusion of control versus "managing in the presence of uncertainty."

**What Does it Mean to Manage in the Presence of Uncertainty**

Uncertainty in plain English is about the “lack of certainty.”

- Uncertainty is about the “variability” in the performance measures like cost, duration, or quality.
- Uncertainty is about the “ambiguity” associated with a lack of this clarity

- This is Deming uncertainty.
- It is the statistical “noise” built into the work process

Both of these sources of uncertainty impact cost and schedule.

- Trying to control the “noise” adds little value.
- Trying to control the “lack of certainty” arising from ambiguity and lack of clarity does have value.

We cannot "Manage in the Presence of Uncertainty" until we know about the statistics. What is the statistical population of allowable values of the random variables of duration and cost for a specific activity? If we don't know these from past performance, we can make inferences to get close.

Once we know about the statistics, we can ask Probability questions.

- What is the probability of completing on or before a specific date?
- What is the probability of this project costing a specific amount or less?