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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Building on a current working papers of David Veenman and myself, and using ‘fancy’ animations, we discuss the issues related to non-random outliers in empirical archival research work and whether robust regression methods can be viewed as a pancea (spoiler: they can’t). Building on these insights we suggest a work-flow for archival work that helps us to take outlier treatment to the next level.

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Designing empirical research to be easily reproducible is no easy task. The new ’treat’ repository, developed by the Open Science Data Center of the TRR 266 Accounting for Transparency, accompanied by a series of short videos, leads the way, step by step.

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