The start of my personal project, analyzing the survivorship of plant plugs in a prairie site, was heading in the right direction, at the right pace. After a couple of weeks, I finally felt like I had a good grasp on what I wanted to ask and analyze. I had an idea of what my abstract/introduction were going to look like, the majority of my methods was written up, and I constructed a method on how to observe/collect the plug survivorship data. However, the second I finished up my data collection, the question of, ‘so how do you want to analyze your data?’, was brought up. I felt like my project came to a sudden halt. My knowledge of analyzing data at a statistical level goes as high as the information taught in stats 100. I could only think of performing a simple T-test. Although that is not a wrong way to analyze my data, it apparently wasn’t the only way. Terms like ANOVA and R, were brought to my attention. These terms were confusing enough to figure out what they even meant, so thinking about how to enter my data into these tests, was terrifying. In all 3 years as an undegrad, I have always been given a set of data, so there was only one correct way to analyze it. If I did take my own measurements, it was either plugged into a linear regression or a simple T-test. Now that I have observed and collected all of my data, there are multiple factors I must consider inputting into a statistical analysis. Words like “coding” or “script” have been put into my everyday vocabulary. It is very intimidating at first, and with the very little experience I have so far, I have learned so much. As someone who went into this whole process of doing their own independent study blindly, I now know the importance of being super organized and recording every little thing you do or state. My advisor told me early in the process to take notes every single day about what I do or hear from other advisors. I thought he was overestimating the whole, ‘everyday’, thing. However, now that I have reached the point where I need to analyze my data and figure out why the plants ended up the way they did, why some survived or some died, it is up to me to answer this question. Therefore, having notes upon notes of little things I noticed out in the field, or important details I learned about the site itself, is super important to have in order to analyze my data properly. There is a reason for why certain plants did better than others, and it is my main question to figure out why this happened. Currently, the whole data analysis process is a love/hate relationship. I am hoping the amount of notes I have taken is sufficient enough for me to figure out what is going on and end up with valid results. I am scared, but ready to take on the challenge of R and ANOVA. The process of putting in the data I collected and expecting a valid relationship or reason why the plants survived/died is what concerns me. I am nervous to see the outcome, or the truth of the matter. However, the steps I take for the analysis and the tools I learn in R will put me a step in the right direction towards future goals.