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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...

Systematic Error: Methodological and Sampling Errors

Systematic Error: Methodological and Sampling Errors

In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
Sampling errors originate from improper sampling methods or the wrong sample population. These errors can be minimized by refining the sampling strategy. Defective instruments or faulty calibrations are the sources of instrumental...

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...

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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...

Reliability and Validity

Reliability and Validity

Reliability and validity are two important considerations that must be made with any type of data collection. Reliability refers to the ability to consistently produce a given result. In the context of psychological research, this would mean that any instruments or tools used to collect data do so in consistent, reproducible ways.

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Assessment of Child Anthropometry in a Large Epidemiologic Study

Assessment of Child Anthropometry in a Large Epidemiologic Study

Published on: February 2, 2017

Multistage evaluation of measurement error in a reliability study.

Aiyi Liu¹, Enrique F Schisterman, Chengqing Wu

¹Division of Epidemiology, Statistics and Prevention Research, National Institute of Child Health and Human Development, NIH/DHHS, 6100 Executive Boulevard, Rockville, Maryland 20852, USA. liua@mail.nih.gov

|December 13, 2006

Summary

New sequential testing methods efficiently assess measurement error in reliability studies. These procedures allow early stopping when measurement reliability meets tolerance levels, saving resources.

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Area of Science:

Biostatistics
Biomarker Research
Reliability Engineering

Background:

Assessing measurement error is crucial for the validity of reliability studies.
Traditional methods may require extensive testing, leading to increased costs and time.
Biomarkers require robust reliability assessment for accurate exposure evaluation.

Purpose of the Study:

To introduce novel sequential testing procedures for planning and analyzing reliability studies.
To develop methods that allow for early termination of studies when measurement error is within acceptable limits.
To provide tabulated critical values for efficient two-stage sequential designs.

Main Methods:

Development of sequential testing procedures for reliability studies.
Incorporation of repeated measurement evaluations within the study design.
Tabulation of critical values for two-stage sequential analysis.
Application of methods to biomarker reliability assessment.

Main Results:

The proposed sequential designs enable efficient evaluation of measurement reliability.
Early stopping rules can be implemented, reducing the need for prolonged testing.
Demonstrated applicability using a case study on oxidative stress biomarkers.

Conclusions:

Sequential testing offers a more efficient approach to reliability studies.
These methods can optimize resource allocation in measurement error assessment.
The developed procedures are valuable for studies involving biomarker reliability.