Visual Inference

Visual Inference is a methodological framework within statistical visualization that enables the drawing of probabilistic conclusions about data patterns through systematic evaluation of visual displays, distinguishing itself from traditional statistical inference by grounding inferential validity in human perception of graphical representations rather than purely mathematical models.

221

Publications

13.5K

Citations

560

Authors

287

Institutions

Publications per year

2017–2026

92

Authors

560

  1. DC
    Dianne Cook

    Iowa State University, United States

    6 publications · 489 citations

    6

    h-index

  2. HH
    Heike Hofmann

    Iowa State University, United States

    6 publications · 489 citations

    6

    h-index

  3. SF
    Steven Franconeri

    Northwestern University, United States

    4 publications · 101 citations

    4

    h-index

  4. MC
    Michael Correll

    Tableau Software (United States), United States

    3 publications · 118 citations

    3

    h-index

  5. DK
    Daniel Kersten

    University of Minnesota, United States

    3 publications · 106 citations

    3

    h-index

1–5 of 560

Venues

  1. IEEE Transactions on Visualization and Computer Graphics

    TVCG · 14 publications · 983 citations

    14

    h-index

  2. arXiv (Cornell University)

    Repository · 10 publications · 364 citations

    10

    h-index

  3. Cognition

    6 publications · 952 citations

    6

    h-index

  4. Frontiers in Psychology

    5 publications · 299 citations

    5

    h-index

  5. Image and Vision Computing

    4 publications · 99 citations

    4

    h-index

1–5