Variability Understanding

Variability Understanding is a methodological construct in data interpretation that identifies and quantifies the extent, sources, and implications of variation within datasets to distinguish systematic patterns from random fluctuations.

41

Publications

2.6K

Citations

88

Authors

52

Institutions

Publications per year

2017–2026

8

Authors

88

  1. DB
    Dani Ben‐Zvi

    University of Haifa, Israel

    3 publications · 213 citations

    3

    h-index

  2. MC
    Michael Conrad

    Wayne State University, United States

    2 publications · 26 citations

    2

    h-index

  3. TB
    Thorsten Berger

    University of Gothenburg, Sweden

    2 publications · 407 citations

    2

    h-index

  4. JG
    Joan Garfield

    University of Minnesota System, United States

    2 publications · 121 citations

    2

    h-index

  5. MB
    Martin Becker

    Fraunhofer Institute for Experimental Software Engineering, Germany

    2 publications · 416 citations

    2

    h-index

1–5 of 88

Venues

  1. Statistics Education Research Journal

    8 publications · 360 citations

    8

    h-index

  2. Technometrics

    2 publications · 174 citations

    2

    h-index

  3. Bulletin of Mathematical Biology

    2 publications · 26 citations

    2

    h-index

  4. Behavior Research Methods

    BRM · 1 publications · 24 citations

    1

    h-index

  5. Experimental Agriculture

    1 publications · 10 citations

    1

    h-index

1–5