Are MtG Spindowns really not that random? Let’s test it /u/pizzatorque DnD: Roll for Initiative!

Today I was bored and saw one of my mtg spindowns (or should I say d20’s hehe) laying around. I read previous posts saying they are not calibrated, not really random because the faces are ordered… blah blah blah.

Well, I wanted first hand empirical evidence and so I decided to roll about 100 times one, collect the results and run not only a chisquared test, but a series of statistical tests to see if any of the null hypotheses (that basically this thing produces results from a random equal distribution) would be broken.

Well, after running the tests it seems that the die might just be good enough to be used in DnD. If anyone wants to chime in and do their own test (maybe collecting 1000 manual rolls instead of 100?), then here is the code included: https://gist.github.com/pizzatorque/bba6db898dcaff4b330cbae915aab5f3

Here are the results from the tests instead:

MannwhitneyuResult(statistic=524281.0, pvalue=0.8848476888914645) KstestResult(statistic=0.037261538461538464, pvalue=0.9978346560818365, statistic_location=5, statistic_sign=-1) Ttest with equal var assumption: TtestResult(statistic=0.1434984964979797, pvalue=0.885899360751111, df=10102.0) Ttest with no equal var assumption: TtestResult(statistic=0.14690506137375514, pvalue=0.8834879602544028, df=105.25966348805969) Levene center median: LeveneResult(statistic=0.1697154111568784, pvalue=0.6803735955560357) Levene center mean: LeveneResult(statistic=0.16166527671207834, pvalue=0.6876364486699228) Power_divergenceResult(statistic=15.23076923076923, pvalue=0.7078216636508432) 

Today I was bored and saw one of my mtg spindowns (or should I say d20’s hehe) laying around. I read previous posts saying they are not calibrated, not really random because the faces are ordered… blah blah blah.

Well, I wanted first hand empirical evidence and so I decided to roll about 100 times one, collect the results and run not only a chisquared test, but a series of statistical tests to see if any of the null hypotheses (that basically this thing produces results from a random equal distribution) would be broken.

Well, after running the tests it seems that the die might just be good enough to be used in DnD. If anyone wants to chime in and do their own test (maybe collecting 1000 manual rolls instead of 100?), then here is the code included: https://gist.github.com/pizzatorque/bba6db898dcaff4b330cbae915aab5f3

Here are the results from the tests instead:

MannwhitneyuResult(statistic=524281.0, pvalue=0.8848476888914645) KstestResult(statistic=0.037261538461538464, pvalue=0.9978346560818365, statistic_location=5, statistic_sign=-1) Ttest with equal var assumption: TtestResult(statistic=0.1434984964979797, pvalue=0.885899360751111, df=10102.0) Ttest with no equal var assumption: TtestResult(statistic=0.14690506137375514, pvalue=0.8834879602544028, df=105.25966348805969) Levene center median: LeveneResult(statistic=0.1697154111568784, pvalue=0.6803735955560357) Levene center mean: LeveneResult(statistic=0.16166527671207834, pvalue=0.6876364486699228) Power_divergenceResult(statistic=15.23076923076923, pvalue=0.7078216636508432)