Thank you, Ione! Yes, agree about the likely reasons. I commented because from the standpoint of equity, the absence of even a small population (particularly one known to experience broad inequities in outcomes) from an advanced learning program seems like something to address. There are almost no districts in the country where AN/AI students comprise a statistically significant proportion, but an equity lens would say their experiences are still significant despite their comparative presence. Seattle has one of the more significant urban AN/AI populations in the country, but even there a single-race data count would hover around 1%.

I just want to say that after taking Ann’s free Dusty Shelf Report course, I feel like I understand better why Molly chose to highlight the striking statistical disparity she did in this example. I don’t want my initial comment to imply that I think these kind of storytelling graphs mean the elimination of key data. I think data on outcome disparities from populations with extreme size differences is actually far better served by this highlighting approach. A separate storytelling graph pulling AN/AI and Pacific Islander student outcomes out would do much more to make inequity visible than the original technical graph did – and given this is a small piece of a longer project, very possible that happened.

I don’t at all! I’m learning from you all about data visualization (looking forward to the course opening up), because I’ve been very reliant on written words and tables in the work I do – which obviously are the opposite of accessible, easily digested information.

In my experience, if we have the freedom to build in an equity lens throughout data collection, we can do things that support that in the eventual report, including presumably visualization. For instance, some school districts are open to using racial demographic data that counts all students who identify with a race “alone or in combination”, which often can build a more complete count and help make visible the differences in smaller subpopulations.

If you don’t get to build the data collection approach from the ground up though, pulling out elements of data revealing inequity that might otherwise be obscured by relative population size could maybe be accomplished through highlighting in pop-out frames and talking about them directly? I know this is just a snippet of a much larger project in Seattle – maybe Molly did that in the original work. The deletion was just notable to me because of the title of the post.

Thanks again to both of you for providing these public resources. Appreciate the opportunity to learn.

Would love to learn from you! Do you have any public-facing data visualization examples you can share?

Yes, the second chart is simpler to read and more visually appealing. Unfortunately, it renders invisible the fact that, despite their existence within the general population of [whatever community this is], Indigenous students have **0%** representation in Advanced Learning. An equity focus would highlight this data and challenge the idea that the statistical size of a population compared to others doesn’t render its disproportional exclusion “insignificant.”

Thanks for these free resources, and for sharing information about making data more visually accessible and powerful.