Nonparametric Tests

Nonparametric Tests is a class of statistical hypothesis testing methods that do not assume a specific distributional form for the underlying population parameters, distinguishing it from parametric tests by relying on fewer assumptions about the data’s probability distribution.

3K

Publications

358.5K

Citations

7.3K

Authors

2.5K

Institutions

Publications per year

2017–2026

472

Variants

Distribution-free TestsNon-parametric Tests

Authors

7.3K

  1. DW
    Donald W. Zimmerman

    Carleton University, Canada

    18 publications · 1.9K citations

    17

    h-index

  2. PK
    Pranab Kumar Sen

    University of North Carolina at Chapel Hill, United States

    18 publications · 1.3K citations

    15

    h-index

  3. JL
    Joseph L. Gastwirth

    George Washington University, United States

    15 publications · 850 citations

    13

    h-index

  4. WJ
    W. J. Conover

    Texas Tech University, United States

    13 publications · 8.8K citations

    13

    h-index

  5. RH
    Ronald H. Randles

    University of Florida, United States

    14 publications · 1.8K citations

    13

    h-index

1–5 of 7.3K

Venues

  1. Journal of the American Statistical Association

    196 publications · 31.3K citations

    67

    h-index

  2. The Annals of Mathematical Statistics

    65 publications · 7.8K citations

    39

    h-index

  3. Biometrics

    63 publications · 7.6K citations

    38

    h-index

  4. The Annals of Statistics

    63 publications · 5.2K citations

    35

    h-index

  5. Technometrics

    48 publications · 19.1K citations

    33

    h-index

1–5