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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.

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...

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...

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...

Distance Corrections

Distance Corrections

To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...

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Robust techniques for measurement error correction: a review.

Annamaria Guolo¹

¹Department of Statistics, University of Padova, Italy. guolo@stat.unipd.it

Statistical Methods in Medical Research

|April 1, 2008

Summary

Measurement error in regression models causes unreliable results. This review explores robust correction techniques with weaker assumptions for improved accuracy in scientific research.

Area of Science:

Statistics
Epidemiology
Biostatistics

Background:

Measurement error in independent variables is a pervasive issue in regression analysis.
Ignoring such errors can lead to substantial unreliability in inferential procedures and research findings.

Purpose of the Study:

To review robust techniques for correcting measurement errors in covariates.
To focus on methods with weaker assumptions and robustness against model misspecifications.

Main Methods:

Systematic review of measurement error correction techniques.
Classification of methods based on modeling assumptions and inferential approaches.
Discussion of applicability and robustness properties.

Main Results:

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Identified various robust techniques for addressing measurement error.
Highlighted the benefits of methods with weaker assumptions for increased robustness.
Grouped techniques by underlying assumptions and inferential strategies.

Conclusions:

Robust methods offer a valuable alternative for handling measurement error when strong assumptions are difficult to verify.
The reviewed techniques enhance the reliability of regression models, particularly in epidemiological contexts.