Do better-looking professors get better student ratings?
Independent rebuild
The study said better-looking professors get higher student ratings. The Lab retraced it: ✓ same result.
Whose code is this?
Our own small program, run on a public copy of the authors' data. It isn't the authors' code. It got 0.2748 against the paper's 0.275, and the other five numbers in that column match too. It's a settled showcase, so there was no forecasting.
See the codeStudent ratings can decide who gets hired and promoted, so it matters what else they pick up.
Cited by 382 studies
We test one thing: does this code and data give this number? That's not the same as the science being right. The forecast price never changes the verdict.
See the proof
Counts as holding up
Where the number comes fromComposite standardized beauty 0.275Table 3, column 1, of the working paper (NBER 9853)The exact claim as posted
Do better-looking professors get better student ratings? Hamermesh and Parker found 0.275 points on a 5-point scale
Hamermesh and Parker (Economics of Education Review, 2005) had people rate professors' looks at the University of Texas at Austin and compared that with students' ratings of 463 courses. One point more on their looks scale went with 0.275 points more on the 5-point course rating, with sex, minority status and other factors held equal. This runs our own small program on a public copy of the authors' data. The rebuild isn't the authors' code, so a mismatch ends as inconclusive.
Do better-looking professors get better student ratings?
- Measured in
- rating points per point of looks
- Paper says
- 0.275
- Counts if
- 0.27 to 0.28
- Lab got
- 0.2748
This result is final
Paper said 0.275. We got 0.2748.
The code and data produced the same result. That doesn't prove the study's method is right.
This slip is the Lab's .
✓ Verified on NEAR. See the contract's history
- People forecast
- Forecasting ends
- The Lab retraces it
- Verdict posted, open to challenge
- Winners collect
What we didn't check
- Whether the sample was fair or large enough.
- Whether test data leaked into training or tuning.
- Whether the result holds on new data.
- Whether the study's method was the right one for its question.
- Whether better teaching and better looks go together for some other reason.
- Whether this holds beyond one university in the early 2000s.
No forecasting on this claim. It was posted with the result already known.
The evidence
See the full receiptHow much evidence is behind this?
Each step is a tougher test. Only the first one decides who wins the market.
- 1. ✓
The Lab's retrace of the code and data named in the claim
The Lab retraced the code and data and got a number inside the claim's range. YES wins.
- 2. –
Stress test: does the number depend on luck?
Fetching the extra runs…
- 3. –
Rebuilt from the description (experimental)
Fetching the AI's rebuild…
Coming next: a data-leakage check, for the same rows in training and test data, and a maths check on the statistics a paper reports. Neither has run on this claim.
Even all three steps can't prove the science is true. They show how well this result stands up to checking.
Verified
Your browser just re-checked these itself, so you don't have to trust this site.
Checking…
What the Lab retraces
- Source
- https://doi.org/10.1016/j.econedurev.2004.07.013
D. S. Hamermesh, A. Parker; rebuild by the RERUN project, Rebuild: MIT. Data: GPL (R package AER), fetched from Rdatasets, not redistributed. - Code, at this exact version
- https://github.com/Maveri-Q/rerun-rebuilds
commit 175eddea88cc8adab8dd20f2100b5b453d359244 - Commands
- python3.11
$ python looks-and-ratings-2005/rebuild.py TeachingRatings.csv > rerun_output.txt && cat rerun_output.txt - Where the result is read
- rerun_output.txt using regex looks_effect (-?[0-9.]+)
- Data files
- TeachingRatings.csv
fingerprint a7d1d8e2a73259faed98ed6abbb3d396b2399c68e59ce6cfc6de94c736c9f1f6 - Limits
- 2 CPU, 4 GB, 15 minutes
- d0b259198cc3c2ace78fbff438c2e074547728ad977b6141efd77f63b76c31dd
Show the claim exactly as stored on the blockchain
{"artifact":{"commit":"175eddea88cc8adab8dd20f2100b5b453d359244","data":[{"path":"TeachingRatings.csv","sha256":"a7d1d8e2a73259faed98ed6abbb3d396b2399c68e59ce6cfc6de94c736c9f1f6","url":"https://vincentarelbundock.github.io/Rdatasets/csv/AER/TeachingRatings.csv"}],"entrypoint":"python looks-and-ratings-2005/rebuild.py TeachingRatings.csv > rerun_output.txt && cat rerun_output.txt","output":{"extractor":{"expr":"looks_effect (-?[0-9.]+)","type":"regex"},"path":"rerun_output.txt"},"repo_url":"https://github.com/Maveri-Q/rerun-rebuilds","runtime":"python3.11","setup":[]},"metric":{"comparison":"within_tolerance","name":"course-rating points per point of the looks score","reported_value":"0.275","tolerance":{"type":"abs","value":"0.005"}},"private_data":null,"resources":{"cpu":2,"max_minutes":15,"mem_gb":4},"source":{"authors":"D. S. Hamermesh, A. Parker; rebuild by the RERUN project","doi":"10.1016/j.econedurev.2004.07.013","license":"Rebuild: MIT. Data: GPL (R package AER), fetched from Rdatasets, not redistributed.","type":"paper","url":"https://doi.org/10.1016/j.econedurev.2004.07.013"},"statement":"Hamermesh and Parker (Economics of Education Review, 2005) had people rate professors' looks at the University of Texas at Austin and compared that with students' ratings of 463 courses. One point more on their looks scale went with 0.275 points more on the 5-point course rating, with sex, minority status and other factors held equal. This runs our own small program on a public copy of the authors' data. The rebuild isn't the authors' code, so a mismatch ends as inconclusive.","title":"Do better-looking professors get better student ratings? Hamermesh and Parker found 0.275 points on a 5-point scale","version":1}Community notes
Notes are people's own words, kept on the blockchain. They never change a verdict, and the Lab's AI never reads them. Posting costs a little NEAR for storage, and what isn't used comes back.