# Zero inflated distributions, what are they really?

I am struggling to understand zero inflated distributions. What are they? What’s the point?

If I have data with many zeroes, then I could fit a logistic regression first calculate the probability of zeroes, and then I could remove all the zeroes, and then fit a regular regression using my choice of distribution (poisson e.g.).

Then somebody told me “hey, use a zero inflated distribution”, but looking it up, it does not seem to do anything differently than what I suggested above? It has a regular parameter $\mu$, and then another parameter $p$ to model the probability of zero? It just does both things at the same time no?

Sometimes this is a necessary evil. Fortunately, it’s not necessary in this case. In R, you can use pscl::hurdle() or fitdistrplus::fitdist().