# The Use of Visualizations to Improve Bayesian Reasoning: A Literature Review

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Methods

## 3. Visualizations That Have Been Effective

## 4. Classroom Teaching of Visualizations to Improve Bayesian Reasoning

^{7}.

## 5. The Creation of New Visualizations

## 6. Adding Interactivity to Visualizations May Be Helpful

## 7. Some Mixed Results

## 8. Consideration of Other Factors When Studying Visualizations

#### 8.1. Natural Frequencies Versus Probabilities

#### 8.2. Problem Format

#### 8.3. Individual Differences

## 9. Future Directions

**Exploring the interaction between certain visualizations and natural frequencies vs. probabilities.**There seems to be a potential for an interesting interaction between the use of (certain) visualizations and the use of natural frequencies vs. probabilities in the problem prompt. Some of the reviewed literature started investigating this possibility, but more studies that use either different visualizations or a wider range of visualizations are needed;- Narrowing down visualizations. As exemplified in Figure 1/Table 1, there are a lot of visualizations that are used in this research area, with some more popular than others. We recommend narrowing down the types of visualizations that are studied so that we can have more knowledge on a smaller set instead of less knowledge over a larger set, and even the standardization of the names of the visualizations (currently there are multiple names to refer to the same thing) to reduce confusion.

- 3.
**Understanding the importance of key features of certain visualizations.**We should be more deliberate in our comparison between different visualizations and choose visualizations (to include in studies) according to either testing competing hypotheses or identifying which features in those visualizations are helpful to students. For example, visualizations can be grouped based on whether quantitative information is presented in a discrete or continuous fashion or based on whether the quantitative information presented is a number, countable item, or area. These features can be used as an independent variable (e.g., area), and the visualizations (e.g., roulette wheel, unit square, probability map) can be chosen to match.

- 4.
**Consideration of other problem formats.**Problem format is not commonly studied or manipulated alongside visualizations but should be considered. Whether problems are framed to be condition-focused (typical format, i.e., the base rate of having a condition/disease is the main focus) or test-focused (i.e., the base rate of a positive/negative test result is the main focus) seems to affect the solution rates across participants [49]. Some considerations are obvious, like making sure that the type of quantitative information (natural frequency vs. probability) matches the problem and the visualization. Bayesian reasoning is typically used in risk assessment and judgment, and with that, problems typically have a negative framing or very serious (health) outcomes. It would be interesting to see studies that manipulate positive vs. negative framing (e.g., test positive/negative vs. is healthy/ill) and see whether this influences over or underestimation. Cover studies of other more relatable contexts, such as social rejection/inclusion, could also be explored.- 5.
**Greater consideration of individual differences.**This literature review identified individual differences as a potential limiting factor for the effectiveness of visualizations. Future studies should investigate which simplifications and modifications (of visualizations and problem prompts) would be helpful to those with low numeracy skills. Additionally, other individual differences that could influence the effectiveness of visualizations or Bayesian reasoning, in general, should be explored. For example, things like risk aversion could bias probability estimates to be over or under the actual probability.- 6.
**Varying level interactivity and use with certain visualizations.**This seems to be the most lacking area of research. We can see this area of research going in two directions: (1) assessing a wide range of interactivity levels or comparing digital to physical manipulations and (2) including interactivity with each of the commonly studied visualizations.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Acknowledgments

## Conflicts of Interest

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**Figure 1.**Types of visualizations used in articles. See Table 1 for key for article letters.

**Table 1.**References corresponding to articles outlined in Figure 1.

Reference ID | Reference |
---|---|

A | Benoy and Rodgers (2007) [8] |

B | Binder et al. (2020) [9] |

C | Binder et al. (2021) [10] |

D | Binder et al. (2015) [11] |

E | Böcherer-Linder & Eichler (2017) [12] |

F | Böcherer-Linder & Eichler (2019) [13] |

G | Brase (2009) [14] |

H | Brase (2014) [15] |

I | Bruckmaier et al. (2019) [16] |

J | Büchter et al. (2022) [17] |

K | Cole (1989) [18] |

L | Cole & Davidson (1989) [19] |

M | Eichler et al. (2020) [20] |

N | Gaissmaier et al. (2012) [21] |

O | Garcia-Retamero et al. (2013) [22] |

P | Gigerenzer and Hoffrage (1995) [1] |

Q | Gigerenzer et al. (2021) [23] |

R | Khan et al. (2015) [2] |

S | Kunzelmann et al. (2022) [24] |

T | Kurzenhäuser and Hoffrage (2002) [25] |

U | Micallef et al. (2012) [26] |

V | Ottley et al. (2016) [27] |

W | Ottley et al. (2019) [28] |

X | Reani et al. (2019) [29] |

Y | Sirota et al. (2014) [30] |

Z | Starns et al. (2019) [31] |

A * | Vogel & Böcherer-Linder (2018) [32] |

B * | Witt & Dhami (2022) [33] |

C * | Wu et al. (2017) [34] |

D * | Yamagishi (2003) [35] |

E * | Zikmund-Fisher et al. (2014) [36] |

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Cui, L.; Lo, S.; Liu, Z.
The Use of Visualizations to Improve Bayesian Reasoning: A Literature Review. *Vision* **2023**, *7*, 17.
https://doi.org/10.3390/vision7010017

**AMA Style**

Cui L, Lo S, Liu Z.
The Use of Visualizations to Improve Bayesian Reasoning: A Literature Review. *Vision*. 2023; 7(1):17.
https://doi.org/10.3390/vision7010017

**Chicago/Turabian Style**

Cui, Lucy, Stephanie Lo, and Zili Liu.
2023. "The Use of Visualizations to Improve Bayesian Reasoning: A Literature Review" *Vision* 7, no. 1: 17.
https://doi.org/10.3390/vision7010017