Didier Brassard
@didierbrassard.bsky.social
28 followers 60 following 23 posts
Professor of nutrition. 💻 Nutritional epidemiology, aging, dietary assessment and causal inference (at least trying) 📍 Université du Québec à Trois-Rivières
Professor of nutrition. 💻 Nutritional epidemiology, aging, dietary assessment and causal inference (at least trying) 📍 Université du Québec à Trois-Rivières
![There is an alternative fully-Frequentist interpretation of confidence intervals that more closely connects them to the idea of null-hypothesis significance testing. The 95% confidence interval contains all possible population parameters that, if they were our null hypothesis, would not be rejected with an alpha of .05 because of our data.[8] One could shorten this to “the confidence interval contains all parameter values that we cannot reject” or “the confidence interval contains all parameter values that are compatible with the data.” The appeal of this is that it’s more snappy than any statement about coverage, while not being wrong according to Frequentist logic. It also does feel like an explanation, although when you think about it, it assumes that you have understood null hypothesis significance testing, and null hypothesis significance testing is confusing in its own right. If you can just assume that people have understood this one complicated thing, can’t you just assume that they have also understood confidence intervals and don’t need any interpretation at all? Then again, if you are deeply confused about the whole matter, maybe this is an elegant solution that buries any confusion one level deeper, where it won’t upset people who really care about confidence intervals.](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:gz53felr7btxgmbxae5aooxi/bafkreidpgoohfrt42m6q2k252acoyufuolz7fl3vjyflseilbi6kr5gsaa)
![Table showing:
Increasing usefulness (and effort)
[Database logo]
1. Curated data available in a findable source (e.g., OSF)
2. Curated data permanently archived in a reputable archive
[Code logo]
1. Workflow documented in text or video for exact reproduction
2. Workflow coded or scripted for automatic reproduction
3. Code/scripts available in a findable source (e.g., GitHub)
4. Code/scripts permanently archived in a reputable archive
[Software blocks logo]
1. Software and dependencies mentioned in a README
2. Specific versions of software and dependencies are mentioned
3. Software and dependencies provided or linked in permanent archive
4. Exact software and dependencies provided in a container
[Computer logo]
1. Computational environment (e.g., operating system) mentioned in a README
2. Exact computational environment provided in a container
[Hardware logo]
1. Hardware requirements described in a README
2. Choose to use specific hardware as agreed in your community](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:yrdy5sepig5zkkvpg2oytaje/bafkreigoaj7iymhrqg3yjc3jffz4mgqi4enbxiyzazosqythitxae4dqdq)
![this is your brain [egg]
this is regression [frying pan]
this is your brain on regression [egg frying in pan]
any questions?](https://cdn.bsky.app/img/feed_thumbnail/plain/did:plc:ta2whs6wjtpljvpqgs5leu36/bafkreihfkuczoevekqismw2qjgpyepgvewbgbmr7uwirdei5ni4dswhs6m)
