Every week, we select a recently published Open Access article to feature. This week’s article is from Stat and demonstrates the fallacy of data-selective inference by examining the  bias in the Erdős–Rényi model. 

The article’s abstract is given below, with the full article available to read here.

Stein, S., & Leng, C. (2022). Fallacy of data-selective inference in modelling networks. Stat, 11( 1), e491. https://doi.org/10.1002/sta4.491

Recent years have seen a growing array of activities in developing statistical models for modelling real-life networks. Since many of these networks are sparse, an all too often practice in the literature is to apply a developed model to a subnetwork typically by discarding nodes due to their lack of connectivity. In this note, we provide the first result highlighting issues with this practice which we call the fallacy of data-selective inference. We demonstrate this fallacy by examining the estimation bias in the Erdős–Rényi model theoretically and in the stochastic block model empirically.