Have you ever felt a statistic in your body?
I remember the first time I felt a statistic with my body. I don’t remember a single other thing: how I learned it, where I was or my age at the time. I remember seeing 2.6 percent of the United States identified as multiracial and my whole body reacted.
Typing it out now my chest still freezes. That’s not the case anymore: the 2020 Decennial Census showed that 10.2 percent of Americans identify as being of two or more races. But in 2010 it was 2.6 percent and in 2000 it was 2.4 percent and they didn’t even allow people to check more than one race before the turn of the millennium so who knows what it was like before I was born.
Data can alienate
Growing up in school it felt like I was always hearing about how America was this great melting pot of different ethnicities. I think of a fondue pot full of cheese, all sorts of aged dairy added to the mix to create a homogenous blend of something new. Each cheese indistinguishable.
Or we could borrow a metaphor from Moti Nissani’s explanation of interdisciplinarity and say I thought of the country like a fruit smoothie: component parts blended into something delicious and new.But it’s really more of a fruit salad, with all different fruits cut up and sloppily churned together. If you take a stab with a fork, who knows what you will fish out. But likely it would be two of the same fruit stuck together.
I don’t think I started counting the number of other mixed race children around me until high school. I had always kept a count of Pakistani people I met — it took me until community college to fill out one hand — but things started to shift in high school, when kids who apply to college begin to find out which stories, which categories, they fit into.
One of the most puzzling categories to me was first-generation American. I was the child of an immigrant, singular. (My dad said I was “the first Mithani born free.”) But that didn’t fit in with many of the other stories about first-generation Americans or children of immigrants I was consuming. I shrugged the confusion off, deciding I wasn’t part of the category, instead of asking where it was I belonged instead.
Data can offer solace
Thinking back on the Census number, I find some comfort. I remember all the times I felt lonely as a kid and now I see an explanation. There’s some hard data to back up those experiences now. I didn’t fit in because there weren’t very many kids like me, on the most basic level. Every time I felt like there wasn’t anyone to talk to, the statistics had my back; I wasn’t making it up.
Reading the press releases in 2021 about the rise in mixed race Americans gave me joy. It made me think about the melting pot again. It made me excited for what the next ten years will hold.
It also keeps me accountable about what stories we tell and whether they match both the facts and experiences of real people. As a journalist I am always weighing the consequences of my actions, forecasting any potential harm my coverage could cause.
Quantitative insights are always a complement to listening to the lived experiences of others. I’m always questioning how we measure things; in addition to population growth a redesign of the Census’s questionnaire to better align with how people identify has been cited as a reason for the jump in mixed race identification.
When I am the data, when someone is measuring me, I always feel it. There was a somatic lurch when I was filling out my sexuality on a form that included two separate options of “bisexual” and “queer, pansexual and/or questioning.”
(I identify firmly as queer, but I didn’t want to be grouped with people who were still figuring out their identity. Why were queer and pansexual also counted as questioning? Did I need to say I was bisexual in order to be definitively counted as gay? Did it really matter if I was in a questioning category is that was all just lumped into an LGBTQ+ meta-category on the backend? I spiraled and the form has remained in an open tab for a week.)
I think about every demographics form that said I could only check one box for race, and how I had to choose “Other” for years. Unnamed hodge-podge categories still make me bristle.
Sometimes we aren’t measuring the right things, or we end up fear-mongering with our questions. I know reducing the overwhelming variety of human experience into checkboxes and intelligible data is difficult. Which is why whenever I approach a story, whenever I am sent a new report, whenever I am designing a survey — I take a breath and imagine who the data is about, and how it might feel to them.
I’m speaking on two panels at NICAR back-to-back this Thursday, March 2. First I will be speaking about bodies and how to find gender-disaggregated data on “When data assumes a male population.” Immediately after I’ll be going over alt-text best practices as a part of “Dataviz accessibility matters — here's what you can do to improve it.”
I am just beaming in for those two panels and won’t be at the conference in person. Both panels have put together tipsheets that will be available to all IRE members on the website, and if you’re not a member I’ll be happy to share the resource with you! (And also maybe use them as newsletter fodder? Let me know.)
I am very late to sharing this but all three entries I submitted were long-listed for the 2022 Information Is Beautiful Awards! I also worked on a project that was short-listed. I’ve never won any awards for my viz work before (and I guess I still haven’t…) but I’m really grateful to have my work recognized among astounding peers.
I’m working on a line of coverage about data gaps at The 19th, and my first story of the year following that theme just published this week. It’s all about the difficulties of obtaining accurate abortion data, how that will change after last year’s SCOTUS verdict, and how it affects public health as a whole. Please give it a read!
Data collection is not neutral! For this series, I want to explore stories of missing data and its effects; gender gaps in collected data and its consequences; grassroots-run surveys; and specific community-led data gathering efforts (all with a gender lens, of course). If you have any ideas or know someone who has a pitch you can comment, reply or email my work address email@example.com.
Rabbit holes 🕳🐇
Tunnels I’ve been scurrying down lately…maybe you’d also like to join? Also more quick-fire updates…I had to have a separate updates section this time because of how long it’s been since I’ve sent a newsletter!
Who The Census Misses: If you’re interested in learning more about people who don’t feel represented in our national tallies, my story written with Alex Samuels from 2021 delves into the history of how race was constructed and counted in America. It has some pretty cool charts! And was long-listed for an IIB award!
I’m working on an internal resource for work about best practices for reporting with data from advocacy organizations. I also want to repurpose it for a talk or a future newsletter — let me know if you have any questions I can address.
We have been in Los Angeles for seven months now and I am beginning to feel settled. We have a weekly pub trivia team, I’m ignoring the last box that needs to be unpacked and it is so beautiful every day.
I’ve been putting off my annual data-driven review of books I read because there was a glitch with my Goodreads account and the finish dates were not recorded correctly. I am waiting for the desire for manual data entry to emerge…and maybe I’m finally starting to feel it?
Thank you all for reading, and sticking with my irregular newsletter pace. The move, while indisputably good, disrupted many of my routines and I wasn’t prepared for how that would affect my creative output. I hope to be in your inbox more regularly.
I would love to hear your stories of somatic statistics. Please reply or comment! I love when my one-way letters get responses.
Funnily enough, it has taken me years to loop back to the idea of something being different and unrelatable to its constituent parts. That’s for a future essay.
Data collection is so far from neutral! I very unfortunately found this is 1000% the case with data on transphobic laws, if that interests you.
A modern example: Abbott's transphobic directive to DFPS is essentially unstudiable since they aren't even recording the investigations (investigators aren't even allowed to mention forced anti-trans investigations in emails or phone calls).
A historic example: only one of the many cities that had masquerade/anti-crossdressing laws in the 1900s seems to have recorded how many people were arrested under the law... And even then, the genders of those arrested is unreliable (for misgendering reasons) and other important information (such as the ethnicity and race of those arrested) is entirely absent.