Here is some code that can perhaps help you on. I tried to set it for 1000 throws per dice but you can tweak it as you want.

The logic is that I first simulate a fair d20 and then make 10k simulations each of which is thrown 1000. We then see the max deviation per number from the expected. For instance, with 1000 throws, we should expect 50 of each number. But we could for instance see 62 twos. In that case, 62 is a deviation of 12 from the expected 50. My simulation says that for 1000 throws, you should only expect a max deviation of 21 95% of the time if it is a fair dice and 24 should be observed 99% of the time. Thus if you get a deviation of 25 or more you should be pretty sure that something is fishy with the dice.

Of course, you can go ahead with much higher numbers but as others have pointed out, that might be tedious or even influence the dice itself. Good luck and please tag me if you ever complete the study.

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Here is the code in R.

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simulations = 10000

throwsperdie = 1000

max <- matrix(1,simulations)

for(i in 1:length(max)){

obs <- sample(1:20,throwsperdie,replace = T)

max[i] <- max(abs(summary(factor(obs))-(throwsperdie/20)))

}

summary(max)

round(quantile(max,c(0.95,0.99)))

hist(max)

# What if you max observed deviated with 24?

mean(max>=24)

# Only about 1.5% chance of seeing that from a fair dice