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

Toxicity Testing in Animals

Toxicity Testing in Animals

Toxicity tests in animals are grounded on two main assumptions: first, the effects observed in laboratory animals can be extrapolated to humans, especially when adjusted for body surface area; second, high-dose exposure in animals is essential to identify potential human hazards from lower doses. This is based on the quantal dose-response concept, which faces the challenge of extrapolating results from relatively few test animals to much larger human populations. For example, a 0.01% incidence...

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.

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% chance...

Contaminants and Errors

Contaminants and Errors

Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...

You might also read

Related Articles

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

Sort by

Same author

Accompaniment in the healthcare sector: a systematic review and concept analysis.

Frontiers in medicine·2026

Same author

The Missing Global South in Climate Change Biology: Towards Equitable Knowledge Inclusion for Effective Global Solutions.

Global change biology·2026

Same author

Two new species of Trichodrilus (Annelida, Clitellata, Lumbriculidae) from Pacific drainages, western USA.

Zootaxa·2025

Same author

Erratum: STEVEN FEND & PILAR RODRIGUEZ (2024) Two new species of Trichodrilus (Annelida, Clitellata, Lumbriculidae) from Pacific drainages, western USA. Zootaxa, 5551 (1): 141-156.

Zootaxa·2025

Same author

Typification and Characterization of Different Livestock Production Systems of Mediterranean Dairy Sheep Farms with Different Degrees of Intensification: A Comparative Study.

Animals : an open access journal from MDPI·2025

Same author

Use of screens, books and adults' interactions on toddler's language and motor skills: A cross-cultural study among 19 Latin American countries from different SES.

PloS one·2025

Search research articles

Related Experiment Video

Updated: Jun 7, 2026

Experimental Protocol for Examining Behavioral Response Profiles in Larval Fish: Application to the Neuro-stimulant Caffeine

Experimental Protocol for Examining Behavioral Response Profiles in Larval Fish: Application to the Neuro-stimulant Caffeine

Published on: July 24, 2018

Evaluating the Type II error rate in a sediment toxicity classification using the Reference Condition Approach.

Pilar Rodriguez¹, Zuriñe Maestre, Maite Martinez-Madrid

¹Department of Zoology and Animal Cell Biology, University of the Basque Country, Bilbao, Spain. pilar.rodriguez@ehu.es

Aquatic Toxicology (Amsterdam, Netherlands)

|October 29, 2010

Summary

This study developed a new method to classify river sediment toxicity using Tubifex tubifex bioassays and statistical analysis. The approach balances Type I and Type II errors, improving sediment quality assessment in Northern Spain.

More Related Videos

A Flow-through Exposure System for Evaluating Suspended Sediments Effects on Aquatic Life

A Flow-through Exposure System for Evaluating Suspended Sediments Effects on Aquatic Life

Published on: January 9, 2017

Vegetated Treatment Systems for Removing Contaminants Associated with Surface Water Toxicity in Agriculture and Urban Runoff

Vegetated Treatment Systems for Removing Contaminants Associated with Surface Water Toxicity in Agriculture and Urban Runoff

Published on: May 15, 2017

Related Experiment Videos

Last Updated: Jun 7, 2026

Experimental Protocol for Examining Behavioral Response Profiles in Larval Fish: Application to the Neuro-stimulant Caffeine

Experimental Protocol for Examining Behavioral Response Profiles in Larval Fish: Application to the Neuro-stimulant Caffeine

Published on: July 24, 2018

A Flow-through Exposure System for Evaluating Suspended Sediments Effects on Aquatic Life

A Flow-through Exposure System for Evaluating Suspended Sediments Effects on Aquatic Life

Published on: January 9, 2017

Vegetated Treatment Systems for Removing Contaminants Associated with Surface Water Toxicity in Agriculture and Urban Runoff

Vegetated Treatment Systems for Removing Contaminants Associated with Surface Water Toxicity in Agriculture and Urban Runoff

Published on: May 15, 2017

Area of Science:

Environmental Toxicology
Ecotoxicology
Aquatic Ecology

Background:

Sediment toxicity assessment is crucial for aquatic ecosystem health.
Traditional methods may not adequately balance detection of toxicity with false positives.
Oligochaete bioassays provide a sensitive measure of sediment contamination.

Purpose of the Study:

To develop and validate a robust method for classifying river sediment toxicity.
To integrate Type I and Type II error rates into sediment toxicity classification.
To establish clear pass-fail boundaries for sediment quality assessment.

Main Methods:

Utilized Tubifex tubifex chronic bioassay on sediments from 71 river sites in Northern Spain.
Employed non-metric multidimensional scaling (MDS) to analyze toxicological endpoints.
Constructed probability ellipses around reference sites and simulated sediment disturbances to quantify Type II error rates.

Main Results:

Established classification criteria for Non Toxic, Potentially Toxic, and Toxic sediments.
Set decision boundaries at the 80% probability ellipse (Non Toxic/Potentially Toxic) and 95% ellipse (Potentially Toxic/Toxic).
Classified 9 sediments as Toxic, 2 as Potentially Toxic, and 13 as Non Toxic using the developed method.

Conclusions:

The developed statistical approach effectively classifies sediment toxicity by considering both error types.
This method enhances the reliability of sediment quality assessments in environmental monitoring.
The findings provide a refined framework for evaluating aquatic ecosystem health based on sediment contamination.