# The Prevalence of Errors in Machine Learning Experiments

arXiv: https://arxiv.org/abs/1909.04436 · HTML: https://mlsanity.com/paper/1909.04436/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Sep 2019.

## Built on by (most cited first, 3 of 3)

- [To Err Is Human: Systematic Quantification of Errors in Published AI Papers via LLM Analysis](https://mlsanity.com/paper/2512.05925/) (2025)
- [The impact of using biased performance metrics on software defect prediction research](https://mlsanity.com/paper/2103.10201/) (2021)
- [Testing the Consistency of Performance Scores Reported for Binary Classification Problems](https://mlsanity.com/paper/2310.12527/) (2023)

## Abstract (arXiv)

Context: Conducting experiments is central to research machine learning research to benchmark, evaluate and compare learning algorithms. Consequently it is important we conduct reliable, trustworthy experiments. Objective: We investigate the incidence of errors in a sample of machine learning experiments in the domain of software defect prediction. Our focus is simple arithmetical and statistical errors. Method: We analyse 49 papers describing 2456 individual experimental results from a previously undertaken systematic review comparing supervised and unsupervised defect prediction classifiers. We extract the confusion matrices and test for relevant constraints, e.g., the marginal probabilities must sum to one. We also check for multiple statistical significance testing errors. Results: We find that a total of 22 out of 49 papers contain demonstrable errors. Of these 7 were statistical and 16 related to confusion matrix inconsistency (one paper contained both classes of error). Conclusions: Whilst some errors may be of a relatively trivial nature, e.g., transcription errors their presence does not engender confidence. We strongly urge researchers to follow open science principles so errors can be more easily be detected and corrected, thus as a community reduce this worryingly high error rate with our computational experiments.