Hmm I definitely think using some basic statistical theory could improve your choice of numbers. A bit of stats provides a lot more information from which you c

Hmm I definitely think using some basic statistical theory could improve your choice of numbers. A bit of stats provides a lot more information from which you can draw more informed decisions. And of course there are lots of results that are highly unintuitive which have real world applications. I think this would fall under "risk engineering" and it is most relevant there. For an example, say you are interested in how frequently a particular function fails. You are especially worried that a first time user will have this function fail on the first attempt and then disengage entirely from your product. So you want to know how likely it is that your function will fail on the first try. You can run an experiment where you call the function repeatedly until it fails. You find it fails on the 10th attempt. You can use knowledge of probability distributions and a trick for calculating the most likely probability given the experiment you conducted to conclude the obvious: that it has a 1/10 chance of failing on the first go. What the long way gets you, is the chance for it failing after x attempts. So a bit of knowledge gets you a lot more information to make informed decisions with. You could always simulate this experiment in a script to estimate these numbers as well, which I think could be useful to someone with a software engineering background but without the time or resources to learn statistics. 0.https://en.wikipedia.org/wiki/Geometric_distribution https://en.wikipedia.org/wiki/Geometric_distribution 1.Probability dist for the example: https://www.wolframalpha.com/input/?i=geometric+distribution+p%3D0.1 https://www.wolframalpha.com/input/?i=geometric+distribution...