By the middle of my LiA, the work had started to feel much more independent. I was spending more time taking a broad policy question and figuring out how to build a clear argument around it. One project involved looking at differences in housing and economic opportunity across New York neighborhoods. I pulled information from public datasets, city reports, previous CUF research, and outside policy sources, then tried to understand which patterns actually mattered. What surprised me was that finding information was not the difficult part. There was almost too much of it. For almost any question, I could find statistics on rent burden, income, homeownership, development, transportation access, or neighborhood change. The harder part was deciding which pieces of evidence actually helped answer the question and which were simply interesting.
I spent a lot of time checking sources, comparing how different reports defined the same measures, and writing short summaries that explained why a number mattered rather than just listing it. That changed how I approached research. In my academic work, I was used to beginning with a specific hypothesis and then testing it. At CUF, I sometimes had to start with a broad problem, see what the available evidence was showing, and then narrow the question from there. I also became much more aware of how misleading a statistic can be without context. By this point in the summer, I was learning that strong policy research was not about including the most information. It was about knowing which information actually helped someone understand the problem.