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Related Concept Videos

Uncertainty: Confidence Intervals00:54

Uncertainty: Confidence Intervals

The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor 't,' or...
Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Model Approaches for Pharmacokinetic Data: Distributed Parameter Models01:06

Model Approaches for Pharmacokinetic Data: Distributed Parameter Models

Pharmacokinetic models are mathematical constructs that represent and predict the time course of drug concentrations in the body, providing meaningful pharmacokinetic parameters. These models are categorized into compartment, physiological, and distributed parameter models.
The distributed parameter models are specifically designed to account for variations and differences in some drug classes. This model is particularly useful for assessing regional concentrations of anticancer or...
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least squares (OLS)...
Growth Models with Integration: Problem Solving01:27

Growth Models with Integration: Problem Solving

In population modeling, integration provides a systematic way to determine accumulated quantities from known rates of change. One such application arises in ecology, where the total weight of a fish population in a body of water is referred to as its biomass. When the rate of growth of this biomass is known as a function of time, calculus can be used to determine the total biomass at a future date.Growth Rate and Biomass FunctionLet the growth rate of the fish population be represented by a...

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Related Experiment Video

Updated: Jun 18, 2026

The Use of an Automated System (GreenFeed) to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals
11:02

The Use of an Automated System (GreenFeed) to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

Published on: September 7, 2015

Management interventions in dairy herds: exploring within herd uncertainty using an integrated Bayesian model.

Martin J Green1, Graham F Medley, Andrew J Bradley

  • 1School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Sutton Bonington, LE12 5RD, United Kingdom. martin.green@nottingham.ac.uk

Veterinary Research
|November 26, 2009
PubMed
Summary

Understanding uncertainty in disease control interventions is crucial for effective farm management. This study highlights significant variability in predicted outcomes for bovine mastitis control, impacting financial benefits and decision-making.

Related Experiment Videos

Last Updated: Jun 18, 2026

The Use of an Automated System (GreenFeed) to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals
11:02

The Use of an Automated System (GreenFeed) to Monitor Enteric Methane and Carbon Dioxide Emissions from Ruminant Animals

Published on: September 7, 2015

Area of Science:

  • Veterinary Medicine
  • Epidemiology
  • Decision Science

Background:

  • Effective disease control on individual farms requires understanding intervention efficacy.
  • Uncertainty in disease control outcomes is often overlooked in veterinary decision-making.
  • Bovine mastitis management presents challenges due to variable intervention effectiveness.

Purpose of the Study:

  • To explore uncertainty in disease incidence and financial benefits from farm management interventions.
  • To quantify the variability in outcomes for two specific bovine mastitis control strategies.
  • To inform veterinary clinicians on managing uncertainty in on-farm health decisions.

Main Methods:

  • Utilized a Bayesian simulation model integrating prior intervention study data.
  • Simulated the impact of dry cow pasture rotation and differential dry cow therapy on 52 farms.
  • Predicted reductions in clinical mastitis incidence within 30 days of calving.

Main Results:

  • Significant uncertainty was found in predicted reductions of clinical mastitis for individual farms.
  • Substantial 95% credible intervals for reduced mastitis incidence indicate clinical relevance.
  • Intervention-attributed mastitis reduction variability led to diverse financial outcomes across farms.

Conclusions:

  • Veterinary decision-making is complicated by outcome uncertainty in farm control measures.
  • Iterative herd health procedures are essential for optimizing individual herd health.
  • Acknowledging and managing uncertainty is key to improving preventive healthcare strategies.