Data Analysis for Experiments

Description from DukeHub

This course covers practical considerations for how to analyze experimental data, with an emphasis on ANOVA and its offshoots (e.g., ANCOVA). A focus is on how design and measurement decisions can inform which statistical techniques are able to be validly conducted. The course also aims to provide intuition and logic behind different analyses so that students can understand what research questions different analyses can (and cannot) answer and which analyses to conduct, given their data and research design. Analyses are conducted in both SPSS and R (as possible). Students can use their preferred language but should be prepared to use (or to learn to use) syntax or scripts for analyses.

Skills

• data and statisticstitle: "data analysis"
• designdescription: "design" and 1 more
• lab workhas a lab component

AI-resilient: 2 of 5 2/5

• ✓Yes: Physical, lab or field work · "laboratory"
• No: Live work with people
• ✓Yes: Judgment someone is accountable for · "decisions"
• No: A craft you practice with your hands or body
• No: Small, in-person format

The checks lean on Microsoft Research, Working with AI (2025), which found generative AI applies least to physical work and hands-on work with people. A signal, not a promise. How the score works

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