Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

Gene Flow

Gene Flow

Gene flow is the transfer of genes among populations, resulting from either the dispersal of gametes or from the migration of individuals.

Epistasis Analysis

Epistasis Analysis

Although Mendel chose seven unrelated traits in peas to study gene segregation, most traits involve multiple gene interactions that create a spectrum of phenotypes. When the interaction of various genes or alleles at different locations influences a phenotype, this is called epistasis. Epistasis often involves one gene masking or interfering with the expression of another (antagonistic epistasis). Epistasis often occurs when different genes are part of the same biochemical pathway. The...

Hypothesis: Accept or Fail to Reject?

Hypothesis: Accept or Fail to Reject?

The outcome of any hypothesis testing leads to rejecting or not rejecting the null hypothesis. This decision is taken based on the analysis of the data, an appropriate test statistic, an appropriate confidence level, the critical values, and P-values. However, when the evidence suggests that the null hypothesis cannot be rejected, is it right to say, 'Accept' the null hypothesis?
There are two ways to indicate that the null hypothesis is not rejected. 'Accept' the null hypothesis and 'fail to...

You might also read

Related Articles

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

Sort by

Same author

Impact of Management Practices on Prevalence of Soybean Sclerotinia Stem Rot in the North-Central United States and on Farmers' Decisions Under Uncertainty.

Plant disease·2019

Same author

Effects of Fluctuating Soil Temperature and Water Potential on Sclerotia Germination and Apothecial Production of Sclerotinia sclerotiorum.

Plant disease·2019

Same author

Explaining loss caused by tomato spotted wilt virus on tobacco with boreal winter weather: a Bayesian approach.

Phytopathology·2010

Same author

A Bayesian approach to meta-analysis of plant pathology studies.

Phytopathology·2010

Same author

Bayesian logistic regression of soybean sclerotinia stem rot prevalence in the u.s. North-central region: accounting for uncertainty in parameter estimation.

Phytopathology·2008

Same author

Logistic regression modeling of prevalence of soybean sclerotinia stem rot in the north-central region of the United States.

Phytopathology·2008

Same journal

<i>Phytophthora cactorum</i>: From Pathogen Biology to Disease Management.

Phytopathology·2026

Same journal

Biometric, Physiological, Biochemical and Molecular Responses of Grapevine to Flavescence Dorée Phytoplasma Infection: A Comprehensive Meta-Analysis.

Phytopathology·2026

Same journal

The Invasive Fungal Pathogen <i>Neopestalotiopsis</i> in North Carolina: Molecular Characterization, Virulence, and Host Susceptibility.

Phytopathology·2026

Same journal

Phloem Sucrose Osmoregulation and Vector Competence in the Asian Citrus Psyllid, the Vector of Huanglongbing.

Phytopathology·2026

Same journal

Compartment-Specific Bacterial Communities in Turmeric and Their Association with Suppression of <i>Ralstonia pseudosolanacearum</i>.

Phytopathology·2026

Same journal

Population Structure of <i>Alternaria brassicicola</i> Suggests Genetic Diversity in Organic Broccoli Farms in Connecticut Is Driven by Multiple Introductions.

Phytopathology·2026

See all related articles

Search research articles

Related Experiment Video

Updated: Jun 28, 2026

Quantification of Fungal Colonization, Sporogenesis, and Production of Mycotoxins Using Kernel Bioassays

Quantification of Fungal Colonization, Sporogenesis, and Production of Mycotoxins Using Kernel Bioassays

Published on: April 23, 2012

Bayesian analysis in plant pathology.

A L Mila, A L Carriquiry

|October 24, 2008

Summary

This summary is machine-generated.

Bayesian inference, a statistical method, is explained using diverse examples. This approach models data and summarizes results using probability distributions for parameters and predictions.

More Related Videos

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

Establishment of a Chilli Thrips (Scirtothrips dorsalis Hood) Rearing System for Virulence Screening of Entomopathogenic Fungi

Establishment of a Chilli Thrips (Scirtothrips dorsalis Hood) Rearing System for Virulence Screening of Entomopathogenic Fungi

Published on: July 18, 2025

Related Experiment Videos

Last Updated: Jun 28, 2026

Quantification of Fungal Colonization, Sporogenesis, and Production of Mycotoxins Using Kernel Bioassays

Quantification of Fungal Colonization, Sporogenesis, and Production of Mycotoxins Using Kernel Bioassays

Published on: April 23, 2012

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Imaging and Analysis for Quantifying Maize (Zea mays) Abiotic Stress Phenotypes

Published on: March 28, 2025

Establishment of a Chilli Thrips (Scirtothrips dorsalis Hood) Rearing System for Virulence Screening of Entomopathogenic Fungi

Establishment of a Chilli Thrips (Scirtothrips dorsalis Hood) Rearing System for Virulence Screening of Entomopathogenic Fungi

Published on: July 18, 2025

Area of Science:

Statistics
Computational Biology
Bioinformatics

Background:

Bayesian methods are increasingly utilized across various scientific disciplines.
Bayesian inference involves fitting probability models to data.
It summarizes findings using probability distributions for model parameters and predictions.

Purpose of the Study:

To introduce the fundamental concepts of Bayesian inference.
To illustrate Bayesian methodology with practical examples.
To showcase the broad applicability of Bayesian approaches.

Main Methods:

Introduction to Bayesian inference principles.
Application of Bayesian methods to biological and experimental design problems.
Demonstration using case studies in genomics and disease mapping.

Main Results:

The paper provides a foundational understanding of Bayesian inference.
Examples illustrate the practical application of Bayesian methodology.
The versatility of Bayesian approaches is highlighted across different fields.

Conclusions:

Bayesian inference is a powerful and adaptable statistical framework.
The presented examples demonstrate its utility in complex scientific research.
Further exploration of Bayesian methods is encouraged for data analysis and experimental design.