It seems that every PM starts with dice or coins to understand both probability and statistics -- mean, variance, sigma, etc
Fair enough; it's not a bad starting point. Everyone has thrown dice or tossed a coin at one time or another.
Here's the things to know if you try to port your experience with games of chance, like dice and coin tosses, into the project domain to address risk and uncertainty.
First, with dice or coins:
- The long term statistics and probability distributions are known, and not amenable to management intervention to change or challenge the outcomes.
- The only uncertainty is the outcome of the next throw; all other statistics are known.
- A long string of outcomes that don't appear random, like HTTTTT for a coin toss of heads or tails, does not suggest a bias
- The long term statistics and probability distributions of risk and uncertainty are NOT usually known, but if they were they ARE amenable to management intervention to change or challenge the outcomes
- The are many uncertainties that could affect the outcome of the risk event; almost all statistics are unknown.
- A long string of outcomes that don't appear random does suggest a bias
Here's why you should know a few things:
- The Central Limit Theorem that predicts a central value among random outcomes can work for you to simplify estimating, and to validate estimates and observations
- Bayes Theorem is a powerful idea about using observations to improve predictions
- The Law of Large Numbers could save you a ton of money and time by showing you that it's not necessary to measure everything
- Expected value is a tool for avoiding many of the flaws of averages while also reducing a lot of data to something meaningful to carry forward to sponsors and non-quantitative managers
- Confidence limits give you some wiggle room to establish credibility but avoid the pitfall of a single point estimate that is almost always wrong.
And, if you want to follow-up, start with the videos at the Khan Academy, probably the best material around for the beginner.
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