Wilk, M.B. and Gnanadesikan, R. 1968.
Probability plotting methods for the analysis of data.
Biometrika 55: 1-17. Jstor link if you have access
This paper is, at the time of my writing, almost 50 years old but still feels fresh and innovative. Using a rich variety of interesting and substantial examples, the authors unify and extend a variety of ideas for plotting and comparing distributions using the framework of Q-Q (quantile-quantile) and P-P (probability-probability) plots. Distributions here mean broadly any sets of data or of numbers (residuals, contrasts, etc., etc.) arising in their analyses.
Particular versions of these plots go back several decades, most obviously normal probability or normal scores plots. which are in these terms quantile-quantile plots, namely plots of observed quantiles versus expected or theoretical quantiles from a sample of the same size from a normal (Gaussian) distribution. But the authors show, modestly yet confidently, that the same ideas can be extended easily -- and practically with modern computing -- for examining other kinds of quantiles and plotting the results automatically.
The authors, then both at Bell Telephone Laboratories, enjoyed state-of-the-art computing facilities, and even many universities and research institutions took a decade or so to catch up. Even now, the ideas in this paper deserve wider application than they get. It's a rare introductory text or course that includes any of these ideas other than the normal Q-Q plot. Histograms and box plots (each often highly useful, but nevertheless each awkward and limited in several ways) continue to be the main staples when plots of distributions are introduced.
On a personal level, even though the main ideas of this paper have been familiar for most of my career, I enjoy re-reading it every couple of years or so. One good reason is pleasure at the way the authors yield simple but powerful ideas to good effect with serious examples. Another good reason is the way that the paper, which is concisely written, without the slightest trace of bombast, hints at extensions of the main ideas. More than once, I've rediscovered twists on the main ideas covered explicitly in side hints and further comments.
This isn't just a paper for those especially interested in statistical graphics, although to my mind that should include everyone interested in statistics of any kind. It promotes ways of thinking about distributions that are practically helpful in developing anyone's statistical skills and insights.