Teaching data analysis to life scientists using “R” statistical software: Challenges, opportunities, and effective methods

Mirra, Renata Medeiros; Vafidis, Jim; Smith, Jeremy A.; Thomas, Robert J.

Abstract

Data analysis is one of the most important and empowering transferable skills that a scientist can possess; yet many life scientists find statistics a daunting subject that they perceive as difficult to master. In this paper, we reflect on our experiences of teaching statistics in a wide variety of contexts in the life sciences. We address the challenge of teaching statistics in general, and of teaching statistics using R in particular, examining several complementary approaches that we have found to be engaging and effective with a diverse range of learners; (1) set-piece taught and practical “lecture-workshop” sessions on specific topics, (2) annotation by learners of template analysis scripts, (3) a user-friendly guidebook with generic script coding that maps onto our other teaching materials, (4) informal, student-led “data analysis clinics”, (5) friendly online support, (6) a dedicated Q&A forum (Facebook “R-Space”) that facilitates peer-to-peer teaching as well as expert input, and (7) video podcast tutorials, enabling independent learning. We consider R to present an important opportunity for enabling “deep learning” (pedagogic meaning) about data analysis, by encouraging users to engage fully with the rationale and detail of statistical methods, designing and implementing appropriate analyses, interpreting them correctly, and reporting them accurately and transparently.

• Online Publication Date: Jun 17, 2023
• Publication Date: Jun 17, 2023
• Deposit Date: Sep 12, 2023
• Publisher: Springer
• Pages: 167-187
• Book Title: Teaching Biostatistics in Medicine and Allied Health Sciences
• ISBN: 9783031260094
• DOI: https://doi.org/10.1007/978-3-031-26010-0_12
• Public URL: https://uwe-repository.worktribe.com/output/10876745
• Additional Information: First Online: 17 June 2023