Do Income Lab's Monte Carlo analysis methods assume a normal distribution/bell curve for returns and inflation?

The Monte Carlo analysis methods in the app produce monthly returns and inflation rates that are independent and identically lognormally distributed. In other words, we don’t inject any assumptions about non-normal skewness or kurtosis ("fat tails"). 

We certainly recognize that this may be overly simplistic and that there may be reasons to assume fat tails, for example, when producing randomized Monte Carlo returns. However, we stick to this simpler approach for two reasons: one analytical and the other practical.

Analytical

When we apply statistical tests to historical monthly market returns and inflation, we have been unable to prove that these returns are NOT normally distributed. (If you’re interested, we apply the Shapiro-Wilk and Shapiro-Francia tests.) That’s not completely conclusive: some analysts do have arguments – maybe even good ones – that monthly returns are not normally distributed, but…

Practical

If we applied skewness and/or excess kurtosis (fat tails), users would have to enter/edit these parameters or accept our defaults, so we would make it much more complicated for firms to enter their assumptions. Usually, firms don’t have assumptions for these parameters, so users may be at a loss. The practical cost of adding this complexity just seems too high, and our approach is certainly reasonable and widely used.