Michigan: Why Michigan

I found the lie in my own data set. Junior year, I built a dashboard tracking eviction filings in my county, scraping court records and mapping them by zip code. The map was clean, the gradient was elegant, and it was misleading. ¹My darkest cluster was not the poorest neighborhood. It was the one whose courthouse digitized records first. I had measured paperwork, not suffering. The numbers were true and the story was false, and no statistics class alone had taught me to notice the difference. ²That is why I am applying to the College of Literature, Science, and the Arts, and specifically why its structure fits the way I have learned to think. I want to major in Statistics and minor in History, and LSA is one of the few places where that pairing is treated as coherent rather than eccentric. I have read the Statistics 415 description on data mining and the practicum in 485, and I want the technical depth. But I am just as drawn to the LSA Honors Program and to courses like History 261 on the United States since 1865, where I can learn to ask who built the archive before I trust what it contains. ³The Quantitative Methods in the Social Sciences program is the bridge I have been looking for. Its requirement that students apply statistical methods to a substantive social question, supervised through a capstone, describes the project I stumbled into alone and did badly. I want to do it again with people who will tell me where I am wrong. ⁴What LSA's breadth offers me is permission to stay confused a little longer before I commit. I do not want to specialize so fast that I forget the eviction map was wrong for a reason that lived outside the spreadsheet. The Race and Ethnicity requirement, which I might have once dismissed as a detour, is now exactly the kind of detour I am seeking, because it forces the context that would have saved my analysis. Outside the classroom, I want to take this to the Michigan Data Science Team, where students partner with local organizations on real problems. I would bring something specific: a hard-earned suspicion of my own clean charts, and the habit of asking what the data leaves out before presenting what it shows. ⁵I came to data because I trusted numbers. I am coming to Michigan because I learned that the most important question a number cannot answer is which numbers were never recorded. LSA is where I can finally learn to ask both halves of that question, and to teach a younger version of myself not to publish the elegant map until he has checked who is missing from it.