Do you need to use statistics to actively and creatively solve real problems that you encounter in your personal or professional lives?
Discover a new methodology for analyzing statistical data through an innovative, simulation-based approach.
You’ll engage in a series of open-ended simulation exercises using Microsoft Excel, which will guide you through the core principles of statistics, including:
- Normal distribution
- Experimental design
- Regression toward the mean
- Sensitivity and Specificity
Many of these concepts are regarded as ‘advanced,’ and only properly addressed in a higher-level statistics course, but we’ll instead treat them as the indispensable core of any genuine engagement with statistics. We can do this through the simulation process, which is key to achieving a concrete, hands-on engagement with these challenging but essential ideas.
No previous experience with statistics is required! We hope that you come to this course with real, urgent problems thrust on you by your own daily work, advocacy, or citizenship.
In completing the simulation exercises, students will:
- Critically evaluate the statistical analyses and claims of others
- Demonstrate statistical principles with detailed simulations
- Explain their statistical reasoning using foundational statistical concepts correctly
- Recognize applicable statistical principles in novel problem settings
Who Should Take this Class?
Prerequisites: No previous experience with statistics is required, but knowledge of using basic formulas in Excel is beneficial.
- Professionals who need to use statistical principles actively and creatively to solve real problems they encounter at work
- Businesspeople who want to be more thoughtful consumers of market research
- Entry-level data scientists who want to advance their careers by gaining greater command over the higher-level principles this work involves
- Citizen activists who want to engage on a higher level with public policy issues
- Anyone who was disappointed by a ‘Stats 101’ experience that lacked in hands-on engagement with real data and real problems