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There appear to be a large number of rules of thumb for histogram bin size and kernel selection for density plots. Are histograms and/or density plots really the best visualization for a single continuous variable? Are there other desirable options? Are there any settled best practices for visualizing continuous data that has no known distribution? Is there a good citable comprehensive reference source in the methods available and their advantages/disadvantages?

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    $\begingroup$ Q-Q plots can be quite useful, but they take some time to learn to 'read' them well. KDE's are not the only choice for density plots (e.g. there's log-spline density estimation). $\endgroup$ – Glen_b Jul 27 '14 at 17:31
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    $\begingroup$ Depends on the sample size too. For really small samples, a dotplot is really probably the best, because densities, bin frequencies, quantiles (or hinges), etc. are just not knowable. $\endgroup$ – rvl Jul 27 '14 at 20:05
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You ask for a citable reference, I would go for one of William Cleveland's book Visualizing Data, also see his author page. See also this earlier questions: https://www.amazon.com/William-S.-Cleveland/e/B000AP9IPS/ref=dp_byline_cont_book_1 and What's a good book or reference for data visualization?.

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