BMB 961-301 – Gaps, Missteps, and Errors in Statistical Data Analysis – Survey of Interest
We are offering a new 5-week course in:

Nov 5 – Dec 5 2018 | MW 12:40-2:00p | 202 Biochemistry Building
Final student presentations: Dec 10 and 12 during regular class hours

Please sign-up below to express your interest, find out if this course is right for you, and provide early input.

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.

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)

This is not an introductory course in statistics or programming. We will assume: 1) Familiarity with basic statistics & probability. 2) Ability to do basic data wrangling, analyses, & visualization using R or Python.
• Strongly recommended MSU courses: CMSE 201 and CMSE 890 Sec 301-or-304 and Sec 302.
• Contact Arjun for pointers to free online preparatory resources.

Arjun Krishnan, Dept. Computational Mathematics, Science and Engineering, Dept. Biochemistry and Molecular Biology | | |

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Programming, Statistics, Bio Background
Describe your background in probability/statistics *
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Describe your background in software for data analysis *
Tell us about tools (e.g. Excel) or programming languages (e.g. R/Python) that you are familiar with for doing statistical data analysis.
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Which are some non-traditional topics in "Statistical Data Analysis" that you would like to see covered in a course like this? *
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Arjun Krishnan

Department of Computational Mathematics, Science and Engineering
Department of Biochemistry and Molecular Biology
Michigan State University

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