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

Predicting Reaction Outcomes

Predicting Reaction Outcomes

Kinetics describes the rate and path by which a reaction occurs. In contrast, thermodynamics deals with state functions and describes the properties, behavior, and components of a system. It is not concerned with the path taken by the process and cannot address the rate at which a reaction occurs. Although it does provide information about what can happen during a reaction process, it does not describe the detailed steps of what appears on an atomic or a molecular level. On the other hand,...

Types of Hypothesis Testing

Types of Hypothesis Testing

There are three types of hypothesis tests: right-tailed, left-tailed, and two-tailed.
When the null and alternative hypotheses are stated, it is observed that the null hypothesis is a neutral statement against which the alternative hypothesis is tested. The alternative hypothesis is a claim that instead has a certain direction. If the null hypothesis claims that p = 0.5, the alternative hypothesis would be an opposing statement to this and can be put either p > 0.5, p < 0.5, or p...

Errors In Hypothesis Tests

Errors In Hypothesis Tests

When performing a hypothesis test, there are four possible outcomes depending on the actual truth (or falseness) of the null hypothesis and the decision to reject or not.

Statistical Hypothesis Testing

Statistical Hypothesis Testing

Hypothesis testing is a critical statistical procedure facilitating informed, evidence-based decisions. It begins with a hypothesis, which is a tentative explanation, or a prediction about a population parameter. This hypothesis can be either a null hypothesis (H0), indicating no effect or difference, or an alternative hypothesis (Ha), suggesting an effect or difference.
Statistical significance measures the probability that an observed result occurred by chance. If this probability, known as...

Accuracy and Errors in Hypothesis Testing

Accuracy and Errors in Hypothesis Testing

Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance,...

You might also read

Related Articles

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

Sort by

Same author

Peer review of the pesticide risk assessment of the active substance cymoxanil.

EFSA journal. European Food Safety Authority·2026

Same author

Peer review of the pesticide risk assessment for the active substance bifenazate in light of confirmatory data submitted.

EFSA journal. European Food Safety Authority·2026

Same author

Peer review of the pesticide risk assessment of the active substance proquinazid.

EFSA journal. European Food Safety Authority·2026

Same author

Peer review of the pesticide risk assessment of the active substance metyltetraprole.

EFSA journal. European Food Safety Authority·2026

Same author

Zebrafish neuromast development: a target for endocrine disrupting chemicals?

Frontiers in toxicology·2026

Same author

Updated peer review of the pesticide risk assessment of the active substance clodinafop (variant evaluated clodinafop-propargyl).

EFSA journal. European Food Safety Authority·2026

Search research articles

Related Experiment Video

Updated: Feb 3, 2026

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

Published on: January 5, 2018

Chemical hazard prediction and hypothesis testing using quantitative adverse outcome pathways.

Edward J Perkins¹, Kalyan Gayen², Jason E Shoemaker³

¹US Army Engineer Research and Development Center, Vicksburg, MS, USA.

|October 18, 2018

Summary

This summary is machine-generated.

Adverse Outcome Pathways (AOPs) can be used as hypotheses for quantitative chemical hazard prediction. This framework integrates in vitro and animal data for improved safety assessments.

Keywords:

quantitative adverse outcome pathways hazard assessment weight of evidence

More Related Videos

Quantitative Autonomic Testing

Quantitative Autonomic Testing

Published on: July 19, 2011

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

Published on: March 14, 2019

Related Experiment Videos

Last Updated: Feb 3, 2026

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

A Prediction Error-driven Retrieval Procedure for Destabilizing and Rewriting Maladaptive Reward Memories in Hazardous Drinkers

Published on: January 5, 2018

Quantitative Autonomic Testing

Quantitative Autonomic Testing

Published on: July 19, 2011

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

A High-throughput Assay for the Prediction of Chemical Toxicity by Automated Phenotypic Profiling of Caenorhabditis elegans

Published on: March 14, 2019

Area of Science:

Toxicology
Chemical Safety
Computational Biology

Background:

Current chemical safety relies on in vitro and animal data within biological pathway contexts.
Quantitative decision-making using these pathways remains a challenge.

Purpose of the Study:

To determine if hypothesis testing with Adverse Outcome Pathways (AOPs) can yield quantitative chemical hazard predictions.
To explore the application of AOPs in chemical risk assessment.

Main Methods:

Reviewed current methods for pathway-based chemical hazard prediction.
Examined case studies and employed computational modeling.
Proposed AOPs as chemically agnostic hypotheses for adverse effects.

Main Results:

Identified three approaches for hypothesis testing with AOPs: weight of evidence, probabilistic, and mechanistic modeling.
Demonstrated hypothesis testing using high-throughput in vitro and alternative animal data.
Discussed standards for regulatory implementation.

Conclusions:

Quantitative AOPs offer a flexible hypothesis framework for predicting chemical hazards.
This approach accommodates diverse methods for progressive quantitative assessment.
AOPs facilitate the integration of various data types for robust chemical safety evaluations.