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fharrell.com | ||
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errorstatistics.com
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| | | | | Stephen Senn Head of Competence Center for Methodology and Statistics (CCMS) Luxembourg Institute of Health Twitter @stephensenn Being a statistician means never having to say you are certain A recent discussion of randomised controlled trials[1] by Angus Deaton and Nancy Cartwright (D&C) contains much interesting analysis but also, in my opinion, does not escape rehashing... | |
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www.fharrell.com
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| | | | | Observational data from electronic health records may contain biases that large sample sizes do not overcome. Moderate confounding by indication may render an infinitely large observational study less useful than a small randomized trial for estimating relative treatment effectiveness. | |
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www.karsdorp.io
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| | | | | I'm a researcher in Computational Humanities and Cultural Evolution at Amsterdam's [Meertens Institute](https://meertens.knaw.nl/index.php/en/), affiliated with the Royal Netherlands Academy of Arts and Sciences. I study aspects of cultural change and experiment with methods to quantify cultural diversity. A significant aspect of my recent work is understanding and accounting for biases in these quantifications. I like to use computational models from fields such as Machine Learning, Cultural Evolution, and Ecology to aid these investigations. Beyond research, I have a passion for teaching computer programming, especially within the Humanities context. Together with [Mike Kestemont](http://mikekestemont.github.io/) and [Allen Riddell](https://www.ariddell.org/), I published the book "Humanities Data Analysis" with Princeton University Press, which guides readers on leveraging Python for analyzing Humanities data. Check out the open access edition [here](https://www.humanitiesdataanalysis.org)! | |
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www.khanna.law
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| | | You want to train a deep neural network. You have the data. It's labeled and wrangled into a useful format. What do you do now? | ||
