# Visual Validation versus Visual Estimation: A Study on the Average Value in Scatterplots

arXiv: https://arxiv.org/abs/2307.09330 · HTML: https://mlsanity.com/paper/2307.09330/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 31 references that are in the map.
- First posted on arXiv 18 Jul 2023.

## Builds on (most cited first, 5 of 5)

- [The State of the Art in Enhancing Trust in Machine Learning Models with the Use of Visualizations](https://mlsanity.com/paper/2212.11737/) (2022)
- [Seeing What You Believe or Believing What You See? Belief Biases Correlation Estimation](https://mlsanity.com/paper/2208.04436/) (2022)
- [The Weighted Average Illusion: Biases in Perceived Mean Position in Scatterplots](https://mlsanity.com/paper/2108.03766/) (2021)
- [How Do We Measure Trust in Visual Data Communication?](https://mlsanity.com/paper/2209.14276/) (2022)
- [Fitting Bell Curves to Data Distributions using Visualization](https://mlsanity.com/paper/2301.04717/) (2023)

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

- [Can AI Mitigate Human Perceptual Biases? A Pilot Study](https://mlsanity.com/paper/2311.00706/) (2023)
- [Beware of Validation by Eye: Visual Validation of Linear Trends in Scatterplots](https://mlsanity.com/paper/2407.11625/) (2024)

## Abstract (arXiv)

We investigate the ability of individuals to visually validate statistical models in terms of their fit to the data. While visual model estimation has been studied extensively, visual model validation remains under-investigated. It is unknown how well people are able to visually validate models, and how their performance compares to visual and computational estimation. As a starting point, we conducted a study across two populations (crowdsourced and volunteers). Participants had to both visually estimate (i.e, draw) and visually validate (i.e., accept or reject) the frequently studied model of averages. Across both populations, the level of accuracy of the models that were considered valid was lower than the accuracy of the estimated models. We find that participants' validation and estimation were unbiased. Moreover, their natural critical point between accepting and rejecting a given mean value is close to the boundary of its 95% confidence interval, indicating that the visually perceived confidence interval corresponds to a common statistical standard. Our work contributes to the understanding of visual model validation and opens new research opportunities.