Tasked with showing the distribution of a certain data set in a different way, I wanted to try to plot a kernel density.
After seeing it however, my co-worker advised against it saying that because it's smooth: "In a histogram the observations are placed in baskets, but in the smooth kernel plot we could pick a value between the two baskets, so we're assuming values that are there that we don't have raw data on."
At the time I couldn't think of a good response (my fundamentals are shaky), but I have seen a handful of kernel density plots in the past and figured there must be a good reason for making them. After doing some research on it and reading Interpretation/use of kernel density, it seems that one does not look at any one "point" but rather the distance between to points of interest and that gives the probability.
Question
If I could go back in time, what would have been a good response? Or are there actually caveats (other than point vs AUC?)?
Further Clarifications:
- utility is not quite as important as aesthetics for this task
- user statistical literacy can assumed to be advanced
- kernel type: Epanechnikov