THE VIDEO SHELF · scientific reproducibility
Is Most Published Research Wrong?
Veritasium explains why a large share of published research findings may be false, covering p-values, p-hacking, publication bias and low replication rates. It draws on John Ioannidis's well-known paper and describes reforms such as preregistration.
Why HN readers recommended it
HN commenters link it whenever a shaky study or the p<0.05 threshold comes up, as a short primer on false positives in science.
This is a summary from the source dataset, not an endorsement by Hacker News or the video creator.
Video & recommendation details
- Creator / channel
- Veritasium
- Topic
- scientific reproducibility
- Format
- explainer
- Level
- general
- HN recommenders in dataset
- 8
- HN mentions in dataset
- 19
- Recommendation years in dataset
- 2017–2025
These counts and years describe the included Hacker News records; they are not YouTube views or the video’s publication date.
From the original HN conversations
Excerpts from the recorded comments. Follow each source for the full comment and surrounding discussion.
If the test data does not have a 50/50 split (or something around that ratio), the headline is straight up lies. Skewed test data is the most common problem with research, reminds me of this great video by Veritasium (Is most published research wrong?): [VIDEO]
r_singh on Hacker News · 2018-02
In Neural nets more accurate than humans at detecting sexual orientation in imagesCue “p = 0.05 is a random choice” and a big thread about p-hacking. Any takers? It feels like everything to say has already been said about the bias of small sample sizes and the dangers of p-hacking. But it also seems like the discussion …
sillysaurusx on Hacker News · 2020-10
In Actively speaking two languages protects against cognitive impairmentPlease watch this video. It neatly shows why should you treat small and less rigorous studies with suspicion, even if there are many giving some results: [VIDEO]
scotty79 on Hacker News · 2021-08
In The Coronavirus Is Here Forever
Summaries, classifications and recommendation records are from the HackerTube source dataset. Read about this library and its limitations.