I need to test if a vector of observed values are uniform distribution.
- This values are not a sample, but my entire universe.
- I have a dataset of 12000 observations, where most of the data (80%) have the same value.
- The range is not delimited.
I'm using this R code to test the uniformity with a significance level of 0.05
Pearson's Chi-squared Test for Count Data
chi2IsUniform=(chisq.test(dataset)$p.value > 0.05)
These results are reliable?
Do I need to use Monte Carlo to compute p-values?
If yes, we have 2 extra options on this function:
*simulate.p.value: a logical indicating whether to compute p-values by Monte Carlo simulation. B: an integer specifying the number of replicates used in the Monte Carlo test. *
How many replicates do I have to use? It depends on my significance level?
chi2IsUniform=(chisq.test(dataset, simulate.p.value = TRUE, B = 1000)$p.value > 0.05)