Individual skill
behavioral-data-analysis
Analyzing behavioral experiment data — RT/accuracy preprocessing, mixed-effects models, and robust inference.
skills/behavioral-data-analysis/SKILL.md
Source commit faf37c0859ebf627bfcb5225e8057d088691e0ae
What was checked
- Matches the observed repository HEAD
- Yes
- Scanner rules are current
- No
- Repository scan complete
- No
- Skill scan complete
- Yes
- Observed behavior
- Not checked yet
- Last checked
- Nov 8, 2026
These are observations at the recorded commit, not a guarantee of safety. Review the source before running it.
Install this skill
Choose an agent in your Stack or copy a command below. Commands fetch the pinned commit and copy only this skill directory. Existing destinations are refused. Copying a command does not install anything.
An install command is unavailable: current rules, complete repository and skill scans, an eligible behavior level and a permitted license are required.
Repository context
- Repository stars (not skill ratings)
- 54
- Repository last updated
- Oct 10, 2026
- License
- MIT
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- ai-agents Repository topic
- ai-agents-automation Repository topic
- ai-skills Repository topic
- awesome Repository topic
- awesome-list Repository topic
- chatgpt Repository topic
- claude-skills Repository topic
- meta-ai Repository topic
- muse-ai Repository topic
- data Derived category
Files and excerpt
File metadata and permitted, redacted excerpts come from the recorded scan. Source links are pinned to that commit.
Browse files
SKILL.mdcore · 5,129 bytes
Read excerpt
## Overview
behavioral-data-analysis covers the practical statistics of human performance data: reaction times,
accuracy, choices, and ratings from psychology experiments. It focuses on the preprocessing
decisions that make or break RT analyses (trimming, transformations), the mixed-effects models that
have replaced repeated-measures ANOVA, and robust inference for the skewed, outlier-prone data
behavioral science produces.
## When to use
- Preprocessing RT data: outlier handling, trimming vs transformations, speed-accuracy trade-offs.
- Accuracy/proportion data: logistic mixed models instead of ANOVA on percentages.
- Repeated-measures designs: linear mixed-effects models with crossed random effects.
- Choice data: logistic regression, drift-diffusion modeling basics.