The main goals for this Biostatistics laboratory manual were to (1) provide direct biological content to each lab session by using authentic research data, and (2) introduce R programming language as the data management and statistical tool in a 200-level Biostatistics course for biology majors. For this, we invited faculty from our Biological Sciences department at California State University-Long Beach to share data with us. As a result, all chapters (except Chapter 1) feature a CSULB biology research lab and use their authentic research data to implement a particular statistical test. By introducing the study system and research questions being addressed by familiar people, we hoped to engaged students and bring a sense of connection to campus and to other students and faculty members. However, the manual was also developed as an open educational resource, free to the students, and fully available to any educator outside campus for implementation.
This Biostatistics laboratory manual was implemented virtually during Fall 2020 but was developed for face-to-face instruction. Thus, it can be implemented both ways. We recommend implementing one chapter per lab session with a follow-up take-home exercise where the students apply the gained statistical and programming knowledge. Ideally, the students would use the same programming tools they implemented in the chapter during the take-home exercise. Such take-home exercises can use authentic or simulated data. Given the technical challenges that may arrive when using computers for programming, we recommend lab sessions of no more than 25 students.
The structure of our semester also included a practical mid-term and a final independent research project in which students needed to generate a research question, analyze it with appropriate statistical and programming tools, and present it orally to the rest of the class in a short presentation.
Table of Content
Chapter 1: Introduction to R and RStudio
Chapter 2. Data sampling, accuracy, and precision. Featured: CNSM Vertebrate Collections
Chapter 3. Visualizing data. Featured: Marine Ecology Lab
Chapter 4. Probability distributions. Featured: Quantitative Ecology Lab
Chapter 5. Hypothesis testing. Featured: Wetlands Ecology Lab
Chapter 6. Population proportions and the binomial distribution. Featured: Avian Ecology Lab
Chapter 7. The normal distribution. Featured: Shark Lab
Chapter 8. Comparing two means: t-test. Featured: Mammal Lab
Chapter 9. One-way anova. Featured: Molecular and ecotoxicology Lab
Chapter 10. Two-way anova. Featured: Marine Ecology Lab
Chapter 11: Correlation and regression analyses. Featured: Microbial Genomics Lab