Dear #dataviz peeps, let& #39;s discuss: Is it ok to differentiate between categories with lightness, or should we use hues?

Different data viz book authors seem to have different opinions. 1/6

2/6 Dona Wong writes in her great book, "The Wall Street Journal Guide to Information Graphics", that shades of one colors are ok. Combinations like blue and orange are not.

3/6 While @visualisingdata writes on p257 in his fantastic Data Vis handbook, 2nd, that "you should not consider using variations in the lightness dimension" for differentiating categories – which means that we shouldn& #39;t even combine hues like yellow and orange.

4/6 Mike Cisneros states something similar in @storywithdata& #39;s latest blog post on picking colors: "For the most part, with categorical data, we should change the hue, and with continuous data or values, we should change the saturation or the lightness." ( http://www.storytellingwithdata.com/blog/2020/5/6/picking-the-right-colors)">https://www.storytellingwithdata.com/blog/2020...

5/6 I myself wrote on https://blog.datawrapper.de/colors/ :">https://blog.datawrapper.de/colors/&q... "Since many readers will associate dark colors with & #39;more& #39; and bright colors with & #39;less& #39;, shades imply a ranking of your categories. Use different hues for your categories to avoid that, and to be able to talk about these colors."

6/6 But maybe I need to go back and revise this article? Maybe there& #39;s research that& #39;s useful in the discussion? Please help, @tamaramunzner @FILWD @AlbertoCairo @jburnmurdoch @sharoz, or whoever knows something. Thank you!