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Related Concept Videos

Systematic Error: Methodological and Sampling Errors01:15

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 Errors01:20

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 Errors01:20

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...
Uncertainty in Measurement: Accuracy and Precision03:37

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.
Random Error01:04

Random Error

Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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)...

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Related Experiment Video

Updated: May 17, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Random variation and systematic error caused by various preanalytical variables, estimated by linear mixed-effects

Marit Sverresdotter Sylte1, Tore Wentzel-Larsen, Bjørn J Bolann

  • 1Laboratory of Clinical Biochemistry, Haukeland University Hospital, Bergen, Norway. marit.sverresdotter.sylte@helse-bergen.no

Clinica Chimica Acta; International Journal of Clinical Chemistry
|November 3, 2012
PubMed
Summary

Specific preanalytical sample handling methods, like pneumatic tube transport and butterfly needles, can significantly impact laboratory test results. These handling variations introduce small but measurable biases and increased variation for certain analytes.

Related Experiment Videos

Last Updated: May 17, 2026

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach
04:35

Development of an Individual-Tree Basal Area Increment Model using a Linear Mixed-Effects Approach

Published on: July 3, 2020

Area of Science:

  • Clinical Chemistry
  • Laboratory Medicine
  • Preanalytical Variables

Background:

  • Preanalytical sample handling is crucial for accurate laboratory test results.
  • Understanding the impact of specific handling techniques on preanalytical variation and bias is essential.

Purpose of the Study:

  • To determine if specific preanalytical sample handling increases variation and bias compared to optimal handling.
  • To evaluate the effects of pneumatic tube transport and needle gauge on blood sample analytes.

Main Methods:

  • Blood samples from 60 outpatients were collected using different needles (21-gauge vs. 23-gauge butterfly) and transport methods (pneumatic tube vs. manual).
  • Sample mixing techniques (5-6 inversions vs. 1 inversion) were also varied.
  • Linear mixed-effects models were employed for statistical analysis.

Main Results:

  • Pneumatic tube transport significantly biased results for lactate dehydrogenase (LD) and magnesium.
  • Increased preanalytical variation was observed for CK and glucose with pneumatic tube transport.
  • Butterfly needles led to lower values for calcium, CK, and LD, and higher variation for ALP compared to 21-gauge needles.

Conclusions:

  • Specific preanalytical sample handling methods have significant, albeit small, effects on certain analyte results.
  • Both random and systematic errors can be introduced by variations in sample transport and collection techniques.