# Understanding distribution chart

How to explain DistributionChart function in mathematica, For example: if I have

nd1 = RandomVariate[NormalDistribution[], 25];
nd2 = RandomVariate[NormalDistribution[2, 10], 25];

DistributionChart[nd1, BarOrigin -> Right]


DistributionChart[nd2, BarOrigin -> Right]


1) what is the importance or application of representing data as distribution chart as compare to Normal distribution 2) How to quantify it? or differentiate from 2nd chart in quantitative way or can i estimate probability of middle fatter region

• Is that a lipstick chart ;) – wolfies Oct 18 '13 at 17:16

You can interpret it as probability density. Here I just put a plot of the pdf in the same graph:

Module[{data = RandomVariate[#, 2000]},
Show[{
Plot[PDF[#, x], {x, Min[data], Max[data]}, PlotRange->All],
DistributionChart[data, BarOrigin -> Left]
}, PlotLabel -> #, PlotRange -> All
]] & /@ {ExponentialDistribution[2.1],
NormalDistribution[0.3, 1.3],
StudentTDistribution[23],
UniformDistribution[{-1, 1}]}


• What I strongly dislike about the distribution chart as well as the smooth histogram is that due to smooth kernel estimation, they show data where there isn't any data. Why is there negative data shown on your ExponentialDistribution example? Showing this as a report to a client could lose me all credibility, if the client thinks that I'm finding negative values where only positive ones can exist. – Gregory Klopper May 16 '18 at 2:45