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Visualization According to Statisticians: An Interview Study on the Role of Visualization for Inferential Statistics

Eric Newburger, Niklas Elmqvist

TL;DR

The paper investigates how professional statisticians use visualization in inferential work, revealing that visualization permeates the entire analytic workflow and shapes mental models of inference. Through 18 semi-structured interviews and thematic analysis, it shows that statisticians favor visually grounded reasoning and resist dichotomous interpretations, guiding the need for multi-faceted visual representations that also convey effect sizes and uncertainty. The study provides concrete design recommendations, such as integrating pre-declared effect-size indicators with traditional statistics and pairing visuals with confirmatory tools to balance intuitive insight with objective evidence. The findings have practical implications for designing visualization tools that support sensemaking in statistics while mitigating over-reliance on any single method or black-box interpretation.

Abstract

Statisticians are not only one of the earliest professional adopters of data visualization, but also some of its most prolific users. Understanding how these professionals utilize visual representations in their analytic process may shed light on best practices for visual sensemaking. We present results from an interview study involving 18 professional statisticians (19.7 years average in the profession) on three aspects: (1) their use of visualization in their daily analytic work; (2) their mental models of inferential statistical processes; and (3) their design recommendations for how to best represent statistical inferences. Interview sessions consisted of discussing inferential statistics, eliciting participant sketches of suitable visual designs, and finally, a design intervention with our proposed visual designs. We analyzed interview transcripts using thematic analysis and open coding, deriving thematic codes on statistical mindset, analytic process, and analytic toolkit. The key findings for each aspect are as follows: (1) statisticians make extensive use of visualization during all phases of their work (and not just when reporting results); (2) their mental models of inferential methods tend to be mostly visually based; and (3) many statisticians abhor dichotomous thinking. The latter suggests that a multi-faceted visual display of inferential statistics that includes a visual indicator of analytically important effect sizes may help to balance the attributed epistemic power of traditional statistical testing with an awareness of the uncertainty of sensemaking.

Visualization According to Statisticians: An Interview Study on the Role of Visualization for Inferential Statistics

TL;DR

The paper investigates how professional statisticians use visualization in inferential work, revealing that visualization permeates the entire analytic workflow and shapes mental models of inference. Through 18 semi-structured interviews and thematic analysis, it shows that statisticians favor visually grounded reasoning and resist dichotomous interpretations, guiding the need for multi-faceted visual representations that also convey effect sizes and uncertainty. The study provides concrete design recommendations, such as integrating pre-declared effect-size indicators with traditional statistics and pairing visuals with confirmatory tools to balance intuitive insight with objective evidence. The findings have practical implications for designing visualization tools that support sensemaking in statistics while mitigating over-reliance on any single method or black-box interpretation.

Abstract

Statisticians are not only one of the earliest professional adopters of data visualization, but also some of its most prolific users. Understanding how these professionals utilize visual representations in their analytic process may shed light on best practices for visual sensemaking. We present results from an interview study involving 18 professional statisticians (19.7 years average in the profession) on three aspects: (1) their use of visualization in their daily analytic work; (2) their mental models of inferential statistical processes; and (3) their design recommendations for how to best represent statistical inferences. Interview sessions consisted of discussing inferential statistics, eliciting participant sketches of suitable visual designs, and finally, a design intervention with our proposed visual designs. We analyzed interview transcripts using thematic analysis and open coding, deriving thematic codes on statistical mindset, analytic process, and analytic toolkit. The key findings for each aspect are as follows: (1) statisticians make extensive use of visualization during all phases of their work (and not just when reporting results); (2) their mental models of inferential methods tend to be mostly visually based; and (3) many statisticians abhor dichotomous thinking. The latter suggests that a multi-faceted visual display of inferential statistics that includes a visual indicator of analytically important effect sizes may help to balance the attributed epistemic power of traditional statistical testing with an awareness of the uncertainty of sensemaking.
Paper Structure (56 sections, 3 figures, 8 tables)

This paper contains 56 sections, 3 figures, 8 tables.

Table of Contents

  1. Introduction
  2. Background
  3. Statistical Inference
  4. Visual Statistics
  5. Everyone a Statistician
  6. Method
  7. Positionality Statement
  8. Participants
  9. Data Collection
  10. Interview Script
  11. Design Probes: Strawman Graphics
  12. Procedure
  13. Pre-interview.
  14. Interview: Preliminaries.
  15. Interview: I - Analytic Process (RQ1).
  16. ...and 41 more sections

Figures (3)

  • Figure 1: Visualization as a bridge. Understanding how professional statisticians use and think about visualization may help designing effective visualizations to support sensemaking for everyone. Note that these characters bear no likeness to the original participants and their quotes have been slightly edited for brevity. (Images by MidJourney v5.)
  • Figure 2: Strawman graphics. The graphics we used as design probes.
  • Figure 3: Graphic elicitation examples. Examples of graphics for statistical inference elicited from participants.