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'?

Thank you for this input! I found this paper answered my question: Graeme D. Ruxton and Markus Neuhauser (2013) Improving the reporting of P-values generated by randomization methods
It seems to say that for a large enough simulated sample, it is acceptable to say that p(equally extreme or more extreme) = number in simulated dataset(equally extreme or more extreme)/size of simulated dataset.
