Biostatistics and R

The purpose of this lesson is to provide context for the science of biostatistics and to highlight a few of the major contributors. Emphasis is given to the role of data analysis for the various disciplines in the biological sciences (e.g., agriculture (animal science, crop science, and soil science), allied health, aquaculture, biology, clinical trials, dentistry, ecology, environmental studies, epidemiology, food production and technology, genetics, health sciences, medicine (allopathic and osteopathic), nursing, nutrition, oceanography, optometry, pharmacy, public health, etc.). The practice of biostatistics is then linked to the use of R, a free and open source software environment. As explained in this introductory lesson, each problem in this text is usually associated with: (1) background of the data, including a description of the data and a stated Null Hypothesis (Ho), (2) a .csv (comma-separated values) dataset, imported into R, (3) a Code Book detailing data organization, (4) visual data checks through the use of simple graphics (e.g., figures), (5) descriptive statistics of factor-type object variables (e.g., frequency distributions) and numeric-type object variables (e.g., mean, sd, median, mode, etc.), (6) quality assurance processes including the use of tests of data distribution and tests for normality, (7) inferential statistical tests, including parametric tests and nonparametric tests, (8) a summary of outcomes (e.g., interpretation of output), (9) multiple addenda addressing additional examples for different types of data and additional functions, practice datasets, and/or bonus materials, and (10) procedures for a graceful exit and save process, to allow later retrieval of the R session.