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Objective To meaningfully organize scientific computing based on evidence gathered through user feedback, build a statistical package based on the findings and provide a replication packet to run similar studies on people with different backgrounds. Method A randomized controlled trial using a weighted, ranked choice survey (n = 118) with between-subjects design having two independent variables: Language Group (Matlab, Python and R) and Method Name options. Our dependent variable was a normalized preference rating. Findings There was a very small interaction between Language Group and Method Name. Language Group did not have a statistically significant effect, but Method Name did (F(4, 27037) = 2211.23, p < .001)(𝜂2 𝑝 = .247). Finally, many names in Matlab, Python and R were ranked so poorly that they were not statistically significantly different from a random word in 63.0%, 62.2% and 30.4% of concepts respectively. Implications We found organized and structured names were ranked by a large margin, suggesting statistical programming today likely needs considerable improvement. Finally, we outline a statistical package built using these principles, provide comparison scripts and describe some of the challenges from going from simple surveys to in-practice libraries. 

Scientific computing has become an area of growing importance. Across fields such as biology, education, physics, or others, people are increasingly using scientific computing to model and understand the world around them. Despite the clear need, almost no systematic analysis has been conducted on how students in fields outside of computer science learn to program in the context of scientific computing. Given that many fields do not explicitly teach much programming to their students, they may have to learn this important skill on their own. To help, using rigorous quantitative and qualitative methods, we looked at the process 154 students followed in the context of a randomized controlled trial on alternative styles of programming that can be used in R. Our results suggest that the barriers students face in scientific computing are non-trivial and this work has two core implications: 1) students learning scientific computing on their own struggle significantly in many different ways, even if they have had prior programming training, and 2) the design of the current generation of scientific computing feels like the wild-wild west and the designs can be improved in ways we will enumerate.