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Gaps, Missteps, and Errors in Statistical Data Analysis

This is a short (1-credit) intermediate-to-advanced course designed to:

  1. Discuss common misunderstandings & typical errors in the practice of statistical data analysis.
  2. Provide a mental toolkit for critical thinking and enquiry of analytical methods and results.

Classes will involve lectures, discussions, hands-on exercises, and homework about concepts critical to the day-to-day use and consumption of quantitative/computational techniques.

Topics include: Underpowered statistics • Pseudoreplication • P-hacking & multiple hypothesis correction • Difference in significance & significant differences • Base rates & permutation tests • Regression to the mean • Descriptive statistics & spurious correlations • Estimation of error and uncertainty • (Others under consideration; Subject to small changes)

[Fall 2019]
[Fall 2018]

Bioinformatics and Computational Biology

This course is an undergraduate- and graduate-level introduction to the inner-workings of methods in bioinformatics and computational biology: analytical techniques, algorithms, and statistical/machine-learning approaches developed to address key questions in biology and medicine. Students will get a variety of opportunities to engage in a number of in-class discussions, discuss-critique-present important papers, work on varied & interesting assignments, and collaborate as learning groups.

By working on a guided semester-long research project, students will also learn how to formulate problems for quantitative inquiry, design computational projects, think critically about data & methods, do reproducible research, and communicate findings.

[Spring 2020]
[Spring 2019]
[Spring 2018]

Modular Courses in Bioinformatics

These courses cover similar material to the August workshops, but use the flipped-classroom philosophy of having students watch video lectures online and come to class to apply the tools to real data. Each module is worth 1 credit.

[Fall 2017]

Bioinformatics Workshops

These bioinformatics workshops provide training in Linux/R/Python programming, Statistical data analysis and visualization, and Analysis of various types of genomic data (e.g. RNA-seq).

[Summer 2017]

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Courses & workshops led by members of the Krishnan Lab.

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