Continuing from my previous post on randomness, I’d like to talk about non-uniform distributions, which certainly don’t get all the love they deserve. When people talk or think about randomness in games, they commonly think about fair distributions. And I know we spent a lot of time in Part 1 actually trying to achieve perfect uniformity because it brings “fairness” to games, but in reality, there are cases where you don’t want your random events to be ruled by a “fair” distribution at all.

# Reality Check #4: Sometimes uniformity is bad

Fun fact: For a lot of “random” events in nature, every possible outcome rarely has the same chance of occurring, so perhaps trying to achieve that uniformity in games could be a mistake to begin with.

Let’s take rabbits for instance.

Yes, rabbits.

If you observe the population of rabbits of a given area, you’ll notice that they’ll have a”typical average size”. Let’s call that size X. You’ll find that **most **rabbits in the area will be **around that size**. There will be a **rare few** that are either** considerably smaller** than X or **considerable bigger **(outliers), but for the most of it, rabbits will be “just around” X in size. Same goes for any other quantifiable trait that depends on enough factors to be considered random.

They will most-likely follow what is called a Normal (or Gaussian) distribution, which is said to appear in nature all the time. The function that defines this distribution is also called the Gaussian** Bell**, due to the shape of the curve.