« Uncertainty is an essential and non-negotiable part of a forecast. …. sometimes an honest and accurate expression of the uncertainty is what has the potential to save [big things]…. However, there is another reason to quantify the uncertainty carefully and explicitly. It is essential to scientific progress, especially under Bayes’s theorem. » – The Signal and the Noise: Why So Many Predictions Fail-but Some Don’t, Nate Silver, from Musing on Project Management, John Goodpasture, author of Project Management: The Agile Way. 

Since all project work operates in the presence of uncertainty, any decisions that need to be made on the project, need to have estimated. This uncertainty is further complicated by scarce resources, changing demands on the project team, changing conditions in the market or project domain, variances in productivity, unanticipated defects, and other stochastic processes found on all projects.

Nate Silver cautions use

In project work, one rarely sees all the data point toward one precise conclusion. Real data is noisy—even if the theory is perfect, the strength of the signal will vary. And under Bayes’s theorem, no theory is perfect. Rather, it is a work in progress, always subject to further refinement and testing. This is what skepticism is all about.

And we should be skeptical about the data from our projects. Reducible and Irreducible uncertainties abound. These create risks to cost, schedule, and technical performance. So the starting point to dealing with these uncertainties is …

Risk Management is How Adults Manage Projects – Tim Lister

And of course, managing risk requires making estimates. 

And while you’re reading John’s book, read Agile!: The Good, The Bad, and the Ugly, which speaks to the unsubstantiated conjectures, carnival hucksters, and other purveyors of out and out fallacies for agile processes, estimates, planning, testing, finance and economics of software development.  Names provided on request.

Source de l’article sur HerdingCats

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