Compare single observed value to simulated distribution I have a distribution of values that I have simulated for a null hypothesis data generating process. I have a single real-world observation that is wayyyy outside the percentiles of this distribution. Can I give a p-value for this single observation not being generated by the same data generating process that gave me the simulated distribution? I'd do something like a Z test, but the simulated distribution isn't quite Normal because it's cut off at 0. Intuitively I feel like I should be able to get a p value by direct comparison to the simulated distribution- because isn't that what other statistical tests do, say 'this is way at the edge of the distribution that would be generated by a certain process'?

Let's assume (a) the $n$ values you generated are numbers and (b) you generated them independently.  Then your null hypothesis is that the real-world value is one more independently generated value.  The chance that it is the most extreme ("way outside") is the chance that it is either the first or the last in the sorted order.
Independence implies every value has a $2/(n+1)$ chance of being most extreme (or more).  Putting aside any concerns about HARKing, data snooping, and so-on, the p-value therefore is $2/(n+1).$

This corresponds to a standard way of estimating p-values using any simulation-based test, such as bootstrapping and permutation tests.
In the general setting the single real-world value is a statistic computed from a dataset and the simulation creates comparable datasets according to the null hypothesis and outputs their statistics.  In most of these applications the interest lies only in one extreme direction -- either very high or very low, but not both -- and the p-value is the proportion of all results (including the real-world value) equal to or more extreme than the real-world value.  Thus, if the real-world value is the $k^\text{th}$ from the extreme value in order out of all $n+1$ values, the associated p-value is $k/(n+1).$
