# Reproducible Research Can Still Be Wrong: Adopting a Prevention Approach

arXiv: https://arxiv.org/abs/1502.03169 · HTML: https://mlsanity.com/paper/1502.03169/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2015.

## Built on by (most cited first, 2 of 2)

- [Leakage and the Reproducibility Crisis in ML-based Science](https://mlsanity.com/paper/2207.07048/) (2022)
- [GIScience in the Era of Artificial Intelligence: A Research Agenda Towards Autonomous GIS](https://mlsanity.com/paper/2503.23633/) (2025)

## Abstract (arXiv)

Reproducibility, the ability to recompute results, and replicability, the chances other experimenters will achieve a consistent result, are two foundational characteristics of successful scientific research. Consistent findings from independent investigators are the primary means by which scientific evidence accumulates for or against an hypothesis. And yet, of late there has been a crisis of confidence among researchers worried about the rate at which studies are either reproducible or replicable. In order to maintain the integrity of science research and maintain the public's trust in science, the scientific community must ensure reproducibility and replicability by engaging in a more preventative approach that greatly expands data analysis education and routinely employs software tools.