Statistical analysis to assess the likelihood that experimental results are significant is a key part of scientific research. The Department of Microbiology & Immunology does not have sufficient faculty expertise or critical mass to offer courses in these topics, but there are many resources available to interested trainees either on or off campus. The Education & Training Committee compiled the following resources. If you are aware of additional resources that could be of benefit to share with our community, please contact our Student Services Specialist Michelle Hightower (michelle_hightower AT med.unc.edu) so we can update this list.
BBSP705 “Best Practices for Rigor and Reproducibility in Research” This introductory class covers topics related to rigor and reproducibility in research. Offered as five 90 minutes classes over two weeks in May each year.
BBSP710 “Introductory Statistics for Laboratory Scientists” Introduces basic concepts and methods of statistics with an emphasis on applications in the experimental biological sciences. Emphasis is on mastery of basic statistical skills and familiarity with situations in which advanced analytical skills may be needed, and on mastery of graphing and statistical analysis of data sets using GraphPad Prism. Tuesday lectures and Thursday Prism workshops. Fall semester. Course website: https://bbsp710.web.unc.edu/
Curriculum in Bioinformatics and Computational Biology offers some biostatistics courses (https://bcb.unc.edu/current-course-schedule/).
The Department of Biostatistics offers relevant courses (https://sph.unc.edu/bios/courses/)
The Department of Statistics and Operations Resources offers some biostatistics courses (https://stat-or.unc.edu/statistics-and-operations-research-courses/statistics-course-description).
The Big Data to Knowledge training program (http://bd2k.web.unc.edu/courses/) summarizes useful courses offered by other departments and also offers modules of their own.
UNC BIOSTATISTICS CONSULTING AND TRAINING
NC TraCS Biostatistics (https://tracs.unc.edu/index.php/services/biostatistics) offers biostatistical collaboration, short-term consultation, statistical methodology support, assistance with grant and manuscript preparation, and education in various topics relevant to biomedical researchers.
UNC Statistical Consulting Center (http://stat-or.unc.edu/consulting/unc-statistical-consulting-center/). The Department of Statistics and Operations Research offers free statistical consulting services, in the context of a course on statistical consulting, STOR 765. This graduate level course aims to both serve the university community, and to instruct students in the art of consulting. Instructor solicits projects at the start of the fall semester.
LCCC Biostatistics Core (https://unclineberger.org/biostats/). The BIOS Core provides a full collaborative scientific resource focused on providing UNC Lineberger members support for the design, conduct, analyses, and generation of manuscripts for their research.
CGBID Biostatistics Core (https://www.med.unc.edu/cgibd/cores/biostatistics/). Starting with consultation, study design, and data management, the Core then assists with the collection, analysis and integration of biological and epidemiological data. It provides assistance with database implementation and maintenance, web applications, validated and secure data capture, quality control, and statistical analysis.
MANUALS AND TEXTBOOKS
GraphPad Prism software (https://www.graphpad.com/scientific-software/prism/). The “Help” materials in GraphPad Prism are really good. Even if you don’t have the software, there are free resources at the website. You can purchase a Prism license from UNC Software Acquisition for $55/year.
An Introduction to Biostatistics Using R by Grover and Mitchell (https://waveland.com/Glover-Mitchell/r-guide.pdf). A gentle introduction to R for biostatistics:
The Fundamentals of Data Visualization by Claus O. Wilke (https://serialmentor.com/dataviz/). Practical resource for quantitative data visualization. Solid overall and available for free online from the author in its entirety.
Biostatistical Analysis by Jerrold Zar
ARTICLES OFFERING PRACTICAL BIOSTATISTICS ADVICE
Pollard DA, Pollard TD, Pollard KS. Empowering statistical methods for cellular and molecular biologists. Mol Biol Cell. 2019; 30(12):1359-68. PMID: 31145670.
Klaus B. Statistical relevance–relevant statistics, part I. EMBO J. 2015; 34(22):2727-30. PMID: 26392568.
Klaus B. Statistical relevance-relevant statistics, part II: presenting experimental data. EMBO J. 2016; 35(16):1726-9. PMID: 27436873.
Richardson BA, Overbaugh J. Basic statistical considerations in virological experiments. J Virol. 2005; 79(2):669-76. PMID: 15613294.
COMMENTARIES FOR BIOLOGISTS ON COMMON STATISTICAL FLAWS
Leek J, McShane BB, Gelman A, Colquhoun D, Nuijten MB, Goodman SN. Five ways to fix statistics. Nature. 2017; 551(7682):557-9. PMID: 29189798.
Weissgerber TL, Milic NM, Winham SJ, Garovic VD. Beyond bar and line graphs: time for a new data presentation paradigm. PLoS Biol. 2015; 13(4):e1002128. PMID: 25901488.
Nuzzo R. Scientific method: statistical errors. Nature. 2014; 506(7487):150-2. PMID: 24522584.
Furuya Y, Wijesundara DK, Neeman T, Metzger DW. Use and misuse of statistical significance in survival analyses. mBio. 2014; 5(2):e00904-14. PMID: 24595371.
Statquest (https://statquest.org). Clear explanations for many stats topics from Josh Starmer, a former faculty member at UNC.
Spurious Correlations (http://www.tylervigen.com/spurious-correlations) provides a good reminder that correlation is not the same as causation.
Statistics for Biologists (https://www.nature.com/collections/qghhqm) is a collection of articles, columns, and resources published in Nature journals.
EXTERNAL TRAINING OPPORTUNITIES
University of Washington Biostatistics Summer Institutes (https://www.biostat.washington.edu/suminst)
University of Michigan Summer Session in Epidemiology (https://sph.umich.edu/umsse/)
STATISTICAL LEARNING/MACHINE LEARNING TEXTBOOKS
Introduction to Statistical Learning by Hastie & Tibshirani. Fantastic (and free) accompanying lecture series (https://www.r-bloggers.com/in-depth-introduction-to-machine-learning-in-15-hours-of-expert-videos/)
Elements of Statistical Learning by Hastie & Tibshirani
Grokking Deep Learning by Trask (https://www.manning.com/books/grokking-deep-learning