Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Videos

Extending the p-plot: heuristics for multiple testing. HNRC group.

I Abramson1, T Wolfson, T D Marcotte

  • 1Department of Mathematics, University of California at San Diego, USA.

Journal of the International Neuropsychological Society : JINS
|November 24, 1999
PubMed
Summary
This summary is machine-generated.

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Biomarkers of Recent Cannabis Use in Blood, Oral Fluid and Breath.

Journal of analytical toxicology·2021
Same author

The Canadian Neuromuscular Disease Registry 2010-2019: A Decade of Facilitating Clinical Research Througha Nationwide, Pan-NeuromuscularDisease Registry.

Journal of neuromuscular diseases·2020
Same author

The Impact of HIV-Associated Neurocognitive Impairment on Driving Performance in Commercial Truck Drivers.

AIDS and behavior·2020
Same author

Parental ancestry and risk of early pregnancy loss at high altitude.

FASEB journal : official publication of the Federation of American Societies for Experimental Biology·2020
Same author

Milk Microbiota: What Are We Exactly Talking About?

Frontiers in microbiology·2020
Same author

Comparison of sporadic and familial behavioral variant frontotemporal dementia (FTD) in a North American cohort.

Alzheimer's & dementia : the journal of the Alzheimer's Association·2020

The p-plot method offers a more revealing alternative to the Bonferroni correction for large-scale multiple testing, effectively estimating false hypotheses. This study enhances the p-plot with bootstrap methods for greater accuracy and provides practical guidelines for its application.

Area of Science:

  • Statistics
  • Biostatistics
  • Neuroscience

Background:

  • Large-scale multiple testing presents challenges, with the Bonferroni method often being too stringent.
  • Graphical methods like the p-plot offer a more revealing approach to assess multiple hypotheses.
  • Existing methods lack robust estimation of uncertainty in identifying false hypotheses.

Purpose of the Study:

  • To elucidate and extend the p-plot method for large-scale multiple testing.
  • To introduce bootstrap techniques for bias and error assessment in p-plot estimates.
  • To provide a confidence interval for the false hypothesis gauge and develop new significance tests.

Main Methods:

  • Application of the p-plot graphical method for multiple hypothesis testing.
  • Utilizing bootstrap resampling to reveal bias and sampling error in point estimates.

Related Experiment Videos

  • Developing and applying bootstrap-based confidence intervals for the false hypothesis gauge.
  • Introducing two new blanket significance tests for hypothesis evaluation.

    • Main Results:

    • The p-plot provides a gauge of decidedly false hypotheses, outperforming the Bonferroni method in stringency and revelation.
    • Bootstrap methods successfully identified bias and sampling error in standard p-plot estimates.
    • A bootstrap-based confidence interval for the false hypothesis gauge was successfully derived.
    • Two novel, acceptably powerful blanket tests of significance were developed and validated.

    Conclusions:

    • The enhanced p-plot method, incorporating bootstrap techniques, offers a more robust and informative approach to large-scale multiple testing.
    • The developed confidence intervals and significance tests provide valuable tools for hypothesis assessment in complex datasets.
    • The study provides practical guidelines and addresses potential pitfalls for the application of the p-plot in real-world research, as demonstrated in an HIV cohort study.