This past week we met with Dr. Melanie Hughes to consider the practical issues of data production, management, and analysis within the social sciences. One thing that struck me was that, despite our ability to identify issues in the categorization systems presented in class, many of us were unable to express solutions to mitigate these gaps in the data. For example, while discussing the readings, one of my main concerns was the binary presentation of gender when approaching gender statistics. This criticism ignored the both practical issues of increasing the number of variables under study (and weakening the power of the statistical measures used) as well as increasing the visibility of minority groups and exposing them to unnecessary violence (Bailey and Gossett, 2018). Here is where I struggle most, as social scientists we use the available data to not only inform us of the present but also reconstruct our ideas of the past. The lack of data on groups that exist outside the constructed norm (or our knowledge at the time) allow people to act as if these groups are new fads or phases that never existed in the past. The anti-vax movement is predicated on this lack of data, with many suggesting that conditions like autism didn’t exist prior to modern vaccines. Of course, this lack of data on autism in the past is due to advances in knowledge, new diagnostic criteria, and new ways of recording and sharing data rather than the condition itself not existing. More specific categories and more data probably won’t solve this issue, so how do we carefully contextualize the data we collect while maintaining the ability to compare it widely with other vastly different contexts?