When you design experiments, you need to know how many participants it takes to get informative results. But what makes results informative? Simply put: A precise effect estimate, meaning an estimate that is unbiased and has a narrow confidence interval. Your randomized design and careful measurement should ensure that your effect estimate is unbiased. But how can you be confident that your estimate’s confidence interval is narrow ’enough'?

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When preparing the analysis for our recent preregistered field experiment on the effect of carbon foodprint labels, we decided to implement an (interactive) specification curve analysis to present insights on how large our researcher degrees of freedom were, even after preregistration. This blog post introduces the idea and links you to the GitHub repo, where you can reproduce and explore our analyis relatively quickly using GitHub Codepaces.

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I recently included the new Our World in Data data on Covid-19 vaccination progress around the world in the {tidycovid19} package. What was meant to be a short info post for package users turned into a mini case on “outliers”. See for yourself

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It started as a Christmas present and ended up to be a fun case study on the productive boost of the open source community. Use R and shiny to hack the LEGO Art Beatles set to become a Stones set!

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Author's picture

Joachim Gassen

Curious researcher, passionate teacher and coding nerd.

Professor of accounting at Humboldt-Universität zu Berlin

Berlin