Do hypotheses about what separates two sets of documents survive a held-out check?
research/qual-hypothesis
Do hypotheses about what separates two sets of documents survive a held-out check? On 5,313 GoEmotions comments a word-counting proposer's effects are 7.6× too large at 100 discovery comments (1.5× at 2,000); a BH check on 3,000 held-out comments confirms almost nothing with shuffled labels, but 36–59% of confirmed hypotheses are the label's own vocabulary. Not an LLM proposer.
What it found
On 5,313 real GoEmotions comments, a word-counting proposer that picks the 20 words most associated with negative emotion in a small discovery sample overstates their effects 7.6× at 100 discovery comments (0.095 against 0.013 held-out) and 1.5× at 2,000. A Benjamini–Hochberg check on 3,000 held-out comments confirms 0.8 of the 20 at 100 comments and 14.7 at 2,000, and with shuffled labels confirms almost none (at most 5.5% of splits any), but 36–59% of confirmed hypotheses are the label's own vocabulary (“sorry”, “hate”), so replication is not explanation. The proposer is not an LLM, and the restatement flag is a crude word list.
A figure from its own output
Paper, code and results
More in Machine learning on qualitative data
- How much of a human emotion codebook does a codebook induced from the text alone recover?How much of a human emotion codebook does a codebook induced from the text alone recover? On 5,313 GoEmotions comments, NMF and k-means codebooks agree with the human emotion label above chance but weakly (NMI 0.26 vs 0.12 for 27 codes); the best single code per emotion reaches AUC about 0.6. Topic models, not an LLM.
- Should you hand-code the items the coder called positive, or sample the audit by the coder's label?Should you hand-code only what the coder called positive, or sample the audit by the coder's label? On 3,188 GoEmotions comments, a positives-only audit had 0 of 1,000 intervals covering the truth for every coder (it misses the coder's false negatives); stratifying by label barely helped. Not an LLM; one corpus.
- Can you audit the coder's negatives at a lower rate and weight them back?Can you audit the coder's negatives at a lower rate and weight them back? On 3,188 GoEmotions comments, weighting removes the bias of a positives-only audit but the interval widens as the negative share falls (0.126 at 50%, 0.352 at 5%, 200 items) and under-covers below ~10%. Not an LLM; coders miss half the positives.
- With six groups, how many hand-coded items does it take for every group's prevalence to be right?With six groups, how many hand-coded items does it take for every group's prevalence to be right? On 3,188 GoEmotions comments, six 95% intervals covered all six truths in only 71 to 77% of 120 to 240-item equal audits (Bonferroni: 89 to 94%); a random audit could not estimate a 1.5% group at all. Not an LLM; one corpus.
- How many of the coder's negatives must you hand-code to recover the positives it missed?How many of the coder's negatives must you hand-code to recover the positives it missed? On 3,188 GoEmotions comments, about 50: coverage was 0.29 with 2, 0.87 with 10 and 0.94 with 50, whatever the audit's total size. Not an LLM; one corpus.
- Which confidence interval is right when only 5 to 20 of the coder's negatives are hand-coded?Which confidence interval is right when you hand-code only 5 to 20 of the coder's negatives? Not Wald: 0.63 coverage at 5 items against 0.94 for Wilson or Jeffreys on 3,188 GoEmotions comments. Not an LLM; one corpus.
All 23 Machine learning on qualitative data projects →
Other research topics
Proper scoring & elicitation28Effort & peer prediction9Verification games & slashing19Detection & tolerances14Markets as routers13Prediction-market design9Paying contributors8Decentralised training systems23Digital twins & sim-to-real164LLM chip design loops40Robot manufacturing loops53Ambient AI assistant46Self-replicating manufacturing34Simulation-first design64Engineered clothing45Task-specific AI accelerators40Algorithmic chip-design loops42Open-source purpose-built robots44The software-engineering loop43Adversarial human imitation41Open-source residential construction41Autonomous multi-angle capture34Sensing, imaging and 3D31Energy supply and dense energy storage39Robot societies among people31Agentic co-design of low-cost robots27Laundry-hanging robots25Machine learning on qualitative data23Robots on the building site27Civilizations & group psychology1History & human sciences1
Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.