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Accuracy and Precision

Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. Highly accurate measurements...

Accuracy and Precision

Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. Highly accurate measurements...

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...

Sensitivity, Specificity, and Predicted Value

Sensitivity, Specificity, and Predicted Value

In healthcare diagnostics, laboratory tests play a crucial role in identifying and diagnosing a wide range of medical conditions. However, interpreting test results is not always straightforward. An abnormal test result does not always confirm the presence of a disease, just as a normal result does not guarantee its absence. To assess the reliability of these diagnostic tools, healthcare practitioners rely on two key statistical indicators: sensitivity and specificity.
Sensitivity is the...

Uncertainty in Measurement: Accuracy and Precision

Uncertainty in Measurement: Accuracy and Precision

Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value.

Statistical Analysis: Overview

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...

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Updated: Jun 3, 2026

Accuracy in Dental Medicine, A New Way to Measure Trueness and Precision

Accuracy in Dental Medicine, A New Way to Measure Trueness and Precision

Published on: April 29, 2014

The value of accuracy in making selection decisions.

H M Klieve¹, B P Kinghorn, S A Barwick

¹Animal Genetics and Breeding Unit1, University of New England, Armidale, Australia Department of Animal Science, University of New England, Armidale, Australia.

Journal of Animal Breeding and Genetics = Zeitschrift Fur Tierzuchtung Und Zuchtungsbiologie

|March 15, 2011

Summary

Including accuracy in genetic selection indices offers flexibility for risk attitudes with minimal impact on genetic response. Optimal weightings balance accuracy and genetic gain, especially for risk-averse or risk-preferring breeders.

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Last Updated: Jun 3, 2026

Accuracy in Dental Medicine, A New Way to Measure Trueness and Precision

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Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

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Published on: October 11, 2018

Area of Science:

Quantitative Genetics
Animal Breeding
Statistical Modeling

Background:

Genetic response and selection utility are crucial in animal breeding programs.
The accuracy of estimated breeding values (EBVs) influences selection decisions.
Understanding the trade-offs between genetic gain and selection utility is essential.

Purpose of the Study:

To investigate the impact of incorporating accuracy into selection indices on genetic response and utility.
To identify optimal index weightings for different population types and utility functions.
To explore the potential for reflecting risk preferences in selection decisions.

Main Methods:

Stochastic simulation with 500 replications was employed.
Two population types (A: 0-1.0 accuracy, B: 0.5-1.0 accuracy) were analyzed.
Index weightings for maximal utility were identified across seven utility functions (risk-averse, risk-preferring, risk-neutral).

Main Results:

Minimal loss in genetic response (<1-5%) occurred with accuracy weightings up to ±0.5σ (Type A) and ±1.5σ (Type B).
These weightings resulted in minor changes in animal rankings, particularly with small selection numbers.
Optimal index weightings for maximal utility varied depending on the specific utility function and population type.

Conclusions:

Weighting accuracy in selection indices can be achieved with little compromise on expected genetic response.
This allows for incorporating risk attitudes (averse or preferring) through adjusted accuracy weightings.
Significant benefits from reflecting extreme utility functions require careful consideration of index construction.