Analyze data quantitatively and draw appropriate conclusions

As biology majors at Longwood, we start gathering and interpreting data in our first class. Because biology–and science in general–is about asking and answering questions through data, the classes many biology classes are heavily focused on data collection and analysis. There are lectures and topics to learn, but every biology class I’ve taken at Longwood has had some sort of lab where we interpret either our own data, data from others, or literature on different topics.

BIOL 120: Biology 120 is the first class biology majors at Longwood take. Because I changed majors in my third semester, I only took this class 2 years ago. That may seem recent, but in rereading my data analysis from this class, I realize how much I’ve learned since then. We only wrote a methods and results section for the experiment we ran in that class, but my results are lacking. Rather than interpreting the data I collected, I simply state it. I know now that referencing graphs and summarizing data is important, but conclusions need to be drawn about what the data actually means in order for experimental results to be significant.

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BIOL 251: Biology 251 is Longwood’s introductory class for ecology. This is the first class where I was introduced to statistical testing for quantitative data in a biological context. I had taken statistics classes, but never had to apply statistical tests to my biology coursework. In this class, we had to design and run a group experiment testing an ecological concept. After collecting our data, our group was tasked with analyzing it with an appropriate statistical test. This was a big step up from my previous experiments where I simply determined if there was a difference in the data, but didn’t look at any statistical significance. We did an ANOVA test which was helpful because our data looked a little scattered, but we were able to show that there was a statistical significance in what we found. This helped us convey the importance of our data during our presentation.

Artifact (link to slides):

BIOL 450: Throughout my entire career as a biology major at Longwood, I’ve had a lot of practice with interpreting quantitative data. One of the biggest projects I’ve done that required data analysis on a large scale was for my biology of cancer class. I took this class last semester, and we had a group project where we used data from cancer genetics databases to determine if certain genetic mutations were statistically significant in determining prognosis of the patient. This required computer analysis of hundreds of genetics mutations within our chosen form of cancer which we were able to achieve with relative ease based on all of our analytical competence. This was definitely the largest data analysis project I’ve taken on in my time at Longwood, and I think both our results and presentation of our results at the research showcase turned out very well. We found statistically significant data to support that some mutations were linked to worse prognoses in glioblastomas.

Artifact (link to slides):