I would tend to disagree with this assessment. Nathan has a Ph.D. in statistics and as someone who is a Ph.D. candidate in the same field, I know for certain he

I would tend to disagree with this assessment. Nathan has a Ph.D. in statistics and as someone who is a Ph.D. candidate in the same field, I know for certain he has done a lot of thinking about this. - For the gaps example, I think it's rather clear what's going on when gaps are present. Is the NYT flowchart not clear? This is valuable advice as a lot of visualization tools (ggplot, d3) will linearly interpolate these values for you so making an effort to avoid that is valuable. - The category example lacks color legends because they are not necessary in this situation. He was simply stating that you can show missingness as a category next to other categorical values. The plots shown are simply examples of the plot type. - Zooming _can_ be biasing but ultimately any visualization of data is going to be some level of 'zoomed' in. As for representing uncertainty, I'm not exactly sure why that is applicable in this case as he's not talking about that. - Interpolating being a bad suggestion, in any case, is... well, just not true. Missing data imputation is a giant subfield of statistics and lots of theory and application has gone into the methods used for it. When it's not a good idea using the 'gaps' suggestion seems like a fair substitute, but there are certainly instances where it is fair to use interpolation. There are absolutely examples for every technique listed where it is not appropriate, but this is more of a toolbox of methods and the visualizer who is familiar with the data should make their decisions on which method to use for their data. Adding this response as the above comment was the only one on the article and I know I sometimes use the comments to filter articles I read and would hate to see someone pass up this due to an (in my opinion) misguided opinion.