Learning to Listen to Data by Emma

Learning to Listen to Data by Emma - July 2026 Scholarship Essay

Before I started my research at the Holden Comprehensive Cancer Center at the University of Iowa, I thought the hardest part would be performing the experiments themselves. Techniques like pipetting, maintaining sterility, and keeping track of protocols — these were the things I expected to struggle with most. I was wrong. The most difficult part came when the data was ready to be analyzed. I was studying how Natural Killer cells activated with monoclonal antibodies influence antitumor activity across different T cell ratios, with and without activation from a bispecific antibody. My mentor and I had designed the experiment carefully, worked meticulously through the calculations, and followed the protocol to the letter. When I sat down to look at the results, what I had expected to be a straightforward analysis with clear trends turned into something far more overwhelming. My research became a lesson in how little I truly knew about critically analyzing results.

My mentor sat with me as we worked through the data together. My initial attempt at extracting anything useful from the data had not led anywhere productive, so he guided me through the process. He pointed out that we had to consider not just the combinatorial effect of the immune cells on the cancer, but also what happened to the immune cells themselves during co-culture. He stopped my spiral of questions with a simple one: "What do the trends suggest is happening?" That question changed how I approached our data. I stopped looking for results that simply proved our hypothesis and started looking for the story in the subtler patterns — even when they didn't align with what we expected. At first, this was uncomfortable. I was used to assignments and exams with one definitive right answer. I slowly learned that real research does not work that way.

That lesson crystallized during a side experiment testing whether Rituximab and Epcoritamab worked synergistically or simply additively. Our dosage matrix experiment showed no single trend — instead, specific dose combinations worked synergistically while others were antagonistic. When we presented our data to the lab manager, I saw what research really looks like. He read our conclusions, offered his own interpretations, and then asked more questions. That single experiment generated three new questions that the lab manager, my mentor, and I all wanted to explore. That was when I realized: data doesn't give us an answer. It gives us the next question.

This experience has reshaped how I approach uncertainty — not as something to eliminate, but as something to work with. As I continue toward a career in medicine and research, I know I will face questions far more complex than T cell ratios. But I am leaving the Holden Comprehensive Cancer Center better equipped to ask them.