Two-sided Tests

Two-Sided Tests is a statistical hypothesis testing framework that belongs to the class of inferential methods designed to evaluate the extremity of a test statistic in both directions relative to the null distribution, distinguishing itself by assessing deviations from the null hypothesis in either tail of the distribution rather than in a single specified direction.

383

Publications

24K

Citations

645

Authors

341

Institutions

Publications per year

2017–2026

33

Variants

Two-tailed Hypothesis TestsTwo-tailed Tests

Authors

645

  1. DW
    Donald W. K. Andrews

    Yale University, United States

    5 publications · 649 citations

    5

    h-index

  2. AM
    Antonio Martín Andrés

    Universidad de Granada, Spain

    5 publications · 90 citations

    5

    h-index

  3. MN
    Markus Neuhäuser

    Koblenz University of Applied Sciences, Germany

    4 publications · 86 citations

    4

    h-index

  4. CW
    Charles W. Dunnett

    McMaster University, Canada

    4 publications · 500 citations

    4

    h-index

  5. PH
    Pao‐Peng Hsu

    Yuanpei University, Taiwan

    4 publications · 204 citations

    4

    h-index

1–5 of 645

Venues

  1. Journal of the American Statistical Association

    38 publications · 2.9K citations

    25

    h-index

  2. Biometrika

    25 publications · 1.5K citations

    19

    h-index

  3. The American Statistician

    19 publications · 831 citations

    16

    h-index

  4. The Annals of Mathematical Statistics

    17 publications · 1.6K citations

    14

    h-index

  5. Biometrics

    12 publications · 1.4K citations

    12

    h-index

1–5