While completing my degree, I have learned to overcome my fear of calculation and quantitative analysis by navigating relevant coursework that inspired me to overcome. With a rather shaky algebraic foundation, many of the calculus-based concepts that were covered were really large hurdles for me to cover in my degree, but with quantitative statistics I was able to incorporate factors from my research that I felt strong and secure in my knowledge of. From there, I could focus on learning the calculation skills that I was not as confident in my proficiency of.

In Document A, I present my final project for my introductory statistics course (Stats 171). Within this project I was introduced to the particular language and pacing necessary to properly articulate trends in numerical data. I learned and applied this formula using the report as a guide throughout other works in the next coming years. Over time, the usage of certain statistical terms and the necessary amounts of detail became second-nature writing skills that I simply turned on when it was necessary to describe the statistical results covered in a specific project or paper. Ultimately my work on this specific lab report, while not uniquely biological in nature, provided the framework that I relied on for the early parts of my biology career and heavily influenced the work I did in classes up until my final semester. These descriptive skills were truly novel and purely born in college (not fostered in my high school education, unlike other skills). To view more about this document, please see Document A below.

In Document B, I present the final report for the laboratory procedures we worked on for the Fall 2022 Research Showcase. I navigated complex linear analysis of data that was often too small to judge with raw values. When working on this results section with my group, I found it a particularly difficult hurdle to synthesize my understanding of statistics and regressions and combine that with my biochemical understanding of what was happening in the methods and reactions. Through working on our individual results sections, many connections began to show themselves and my understanding of the overlaps between math and science were greatly polished. To learn more about this project, please view Document B in the section below.

In Document C, I evaluated the numerical data presented by other researches and worked hard to interpret their usages and meaning. In order to process these data, I had to learn to interpret graphs far more complex than what I would have encountered in my undergrad. The statistics were heavy as they analyzed country-sized populations, and often enough the statistics were presented in plot formats I had never been introduced to. In order to properly support moy own claims using the data of other researchers, I relied on my own quantitative reasoning and improved upon my graphical recognition by teaching myself the structures and plots I was viewing. I think that this process truly introduced me to myself in a self-teaching self-learning fashion and taught me much more about how I learn than any other endeavor. Projects with similar work were easier due to this experience. In order to learn more about this text, please view Document C below.