The Importance Of Distributions

Jocelyn Barker explains distributions using role-playing games as an example:

We see that for the entire curve, our odds of success goes down when we add criticals and for most of the curve, it goes up for 3z8. Lets think about why. We know the guards are more likely to roll a 20 and less likely to roll a 1 from the distribution we made earlier. This happens about 14% of the time, which is pretty common, and when it happens, the rogue has to have a very high modifier and still roll well to overcome it unless they also roll a 20. On the other hand, with 3z8 system, criticals are far less common and everyone rolls close to average more of the time. The expected value for the rogue is ~10.5, where as it is ~14 for the guards, so when everyone performs close to average, the rogue only needs a small modifier to have a reasonable chance of success.

It’s a nice spin on a classic statistics lesson.

Related Posts

Using The Azure Data Science VM With GPUs

Jennifer Marsman has some tips and tricks around using the Azure Data Science Virtual Machine on an instance running with GPU support: To get GPU support, you need both hardware with GPUs in a datacenter, as well as the right software – namely, a virtual machine image that includes GPU drivers so you can use […]

Read More

Visualizing Model Input Effects

Ilknur Kaynar Kabul shows us how to use partial dependence plots and individual conditional expectation plots to view the specific effect of an input variable on a model: A partial dependence (PD) plot depicts the functional relationship between a small number of input variables and predictions. They show how the predictions partially depend on values […]

Read More


November 2017
« Oct Dec »