Beautiful, Bianca, such a nice idea! If you look at the data you feeding into the Histogram, you may notice

data = -Subtract @@@ Values[Normal[yearsanddate[All, 
{"ArtistBio" -> (ToExpression[#[[1]]] &), "Date" -> Identity}]]];

MinMax[data]

{-75, 1968}

that min and max values are not the ages. Taking numbers only between 18 and 100 and hoping those are mostly ages:

ages = Select[data, 18 < # < 100 &];

we can find the distribution describing the data using machine learning function FindDistribution:

dis = FindDistribution[ages]

BorelTannerDistribution[0.6432, 16]

Curiously, it is related to branching processes and queueing theory and fits pretty nicely:

Show[
 Histogram[ages, Automatic, "PDF", PlotTheme -> {"Detailed", "Large", "LargeLabels"}], 
 DiscretePlot[PDF[dis, x], {x, 0, 100}, PlotRange -> All, PlotMarkers -> Automatic]]