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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...

Random and Systematic Errors

Random and Systematic Errors

Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...

Random and Systematic Errors

Random and Systematic Errors

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...

Accuracy and Precision

Accuracy and Precision

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Author Spotlight: Assessing the Reliability of Doppler Ultrasound in Measuring Leg Blood Flow

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Estimators of repeatability.

H Mansour¹, E V Nordheim, J J Rutledge

¹University of Wisconsin, Madison, Wis., USA.

TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik

|November 27, 2013

Summary

This summary is machine-generated.

This study compared six methods for estimating repeatability. Maximum likelihood estimation is generally preferred, especially when the repeatability is small or variances are unequal, with structural analysis on the correlation matrix showing the best performance.

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Area of Science:

Statistics
Quantitative Genetics

Background:

Estimating repeatability is crucial in various scientific fields.
Existing methods like ANOVA and principal components analysis (PCA) have limitations.

Purpose of the Study:

To compare the performance of six different estimation procedures for repeatability.
To identify the most robust and accurate method under various assumptions.

Main Methods:

Comparison of six estimation procedures: ANOVA, PCA (covariance and correlation matrices), structural analysis (covariance and correlation matrices), and maximum likelihood estimation.
A simulation study was conducted to evaluate estimator performance.
Assumptions of equal variance and standard linear models were tested.

Main Results:

Estimators showed similar performance when standard linear model assumptions were met, except for small repeatability values.
Maximum likelihood estimation was generally preferred.
When the equal variance assumption was relaxed, methods using the sample correlation matrix performed better.
Structural analysis using the sample correlation matrix demonstrated superior performance.

Conclusions:

Maximum likelihood estimation is a reliable method for estimating repeatability.
Structural analysis, particularly using the sample correlation matrix, offers the best performance, especially when assumptions are violated.