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Abstract

Thought experiments based on simulation can be used to explain the impact of the chosen study design, statistical analysis strategy, or the sensitivity of results to fellow researchers. In this article, we demonstrate with two examples how to implement quantitative thought experiments in Stata. The first example uses a large-sample approach to study the impact on the estimated effect size of dichotomizing an exposure variable at different values. The second example uses simulations of datasets of realistic size to illustrate the necessity of using sampling fractions as inverse probability weights in statistical analysis for protection against bias in a complex sampling design. We also give a brief outline of the general steps needed for implementing quantitative thought experiments in Stata. We demonstrate how Stata provides programming facilities for conveniently implementing such thought experiments, with the advantage of saving researchers time, speculation, and debate as well as improving communication in interdisciplinary research groups.

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