# Can you Trust the Trend: Discovering Simpson's Paradoxes in Social Data

arXiv: https://arxiv.org/abs/1801.04385 · HTML: https://mlsanity.com/paper/1801.04385/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2018.

## Built on by (most cited first, 5 of 5)

- [Causal Collaborative Filtering](https://mlsanity.com/paper/2102.01868/) (2021)
- [DoGR: Disaggregated Gaussian Regression for Reproducible Analysis of Heterogeneous Data](https://mlsanity.com/paper/2108.13581/) (2021)
- [Resolution of Simpson's paradox via the common cause principle](https://mlsanity.com/paper/2403.00957/) (2024)
- [De-paradox Tree: Breaking Down Simpson's Paradox via A Kernel-Based Partition Algorithm](https://mlsanity.com/paper/2603.02174/) (2026)
- [FAIRLABEL: Correcting Bias in Labels](https://mlsanity.com/paper/2311.00638/) (2023)

## Abstract (arXiv)

We investigate how Simpson's paradox affects analysis of trends in social data. According to the paradox, the trends observed in data that has been aggregated over an entire population may be different from, and even opposite to, those of the underlying subgroups. Failure to take this effect into account can lead analysis to wrong conclusions. We present a statistical method to automatically identify Simpson's paradox in data by comparing statistical trends in the aggregate data to those in the disaggregated subgroups. We apply the approach to data from Stack Exchange, a popular question-answering platform, to analyze factors affecting answerer performance, specifically, the likelihood that an answer written by a user will be accepted by the asker as the best answer to his or her question. Our analysis confirms a known Simpson's paradox and identifies several new instances. These paradoxes provide novel insights into user behavior on Stack Exchange.