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isaacslavitt.com | ||
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simkovic.github.io
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| | | | | [AI summary] This post discusses the limitations of using raw score differences in ordinal data analysis, particularly when dealing with ceiling effects. The author demonstrates that raw score differences can be biased towards zero and have reduced precision in boundary regions. They advocate for using logit-based models to accurately estimate treatment effects while accounting for ordinal data structure and ceiling effects. The post includes simulations showing how ceiling effects can reduce the detectability of true effects and highlights the importance of using appropriate statistical models to avoid biased conclusions. | |
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articles.foletta.org
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weisser-zwerg.dev
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| | | | | A series about Monte Carlo methods and generating samples from probability distributions. | |
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sebastianraschka.com
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| | | I'm an LLM Research Engineer with over a decade of experience in artificial intelligence. My work bridges academia and industry, with roles including senior staff at an AI company and a statistics professor. My expertise lies in LLM research and the development of high-performance AI systems, with a deep focus on practical, code-driven implementations. | ||
