Statistical Power Analysis

Statistical Power Analysis is a methodological procedure used in candidate gene studies to determine the probability that a statistical test will correctly reject a false null hypothesis, thereby distinguishing it from other analytical methods by its specific focus on detecting genetic effects with predefined sensitivity and minimizing Type II error rates.

4.4K

Publications

845.8K

Citations

16.5K

Authors

3.9K

Institutions

Publications per year

2017–2026

1.2K

Authors

16.5K

  1. HZ
    Hongyu Zhao

    Yale University, United States

    15 publications · 891 citations

    13

    h-index

  2. CV
    Conor V. Dolan

    University of Amsterdam, Netherlands

    14 publications · 1.1K citations

    13

    h-index

  3. HA
    Herman Aguinis

    George Washington University, United States

    13 publications · 4.5K citations

    13

    h-index

  4. JC
    Jacob Cohen

    New York University, United States

    13 publications · 48.8K citations

    12

    h-index

  5. KE
    Keith E. Muller

    University of North Carolina at Chapel Hill, United States

    12 publications · 101.1K citations

    12

    h-index

1–5 of 16.5K

Venues

  1. PLoS ONE

    100 publications · 6.8K citations

    40

    h-index

  2. Statistics in Medicine

    82 publications · 8K citations

    39

    h-index

  3. Bioinformatics

    78 publications · 13.1K citations

    38

    h-index

  4. Genetics

    46 publications · 7.4K citations

    35

    h-index

  5. PubMed

    Repository · 102 publications · 16.8K citations

    34

    h-index

1–5