Bayesian confidence intervals for RateMyProfessors ratings (inspired by 3Blue1Brown)
Project description
rmp-ci
Bayesian confidence intervals for RateMyProfessors ratings.
Inspired by 3Blue1Brown's video on binomial distributions and the beta distribution — why you shouldn't trust online ratings at face value.
The Problem
A professor with 2 ratings averaging 5.0/5 looks better than one with 200 ratings averaging 4.5/5. But the first professor's true quality is far more uncertain. This tool computes a Bayesian confidence interval using the Beta distribution to quantify that uncertainty.
Install
pip install rmp-ci
# or
uv tool install rmp-ci
# or
pipx install rmp-ci
Usage
rmp-ci "Chao Zhang" -s "Georgia Tech"
rmp-ci "Mark Riedl" -s "Georgia Tech"
rmp-ci "Lingming Zhang" -s "UIUC"
# JSON output for piping
rmp-ci "Chao Zhang" -s "Georgia Tech" --json | jq .bayesian
# Custom confidence level
rmp-ci "Chao Zhang" -s "Georgia Tech" -c 0.90
How It Works
- Fetches the rating distribution (1-5★) from RMP's GraphQL API
- Treats 4-5★ as "positive" and 1-3★ as "negative"
- Applies a Beta(1,1) prior (uniform / Laplace)
- Computes the posterior Beta(positive+1, negative+1)
- Reports the 95% credible interval for P(positive rating)
Zero dependencies — pure Python stdlib.
License
MIT
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