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Related Concept Videos

Variability: Analysis01:11

Variability: Analysis

Measures of variability are statistical metrics that reveal the dispersion pattern within a dataset. They are pivotal in biostatistics, providing insights into the heterogeneity within health and biological data. Variability signifies the degree to which data points diverge from one another, helping researchers understand the potential range of values and associated uncertainty within the data.
The range is a simple measure of variability, indicating the difference between the highest and...
Propagation of Uncertainty from Systematic Error01:10

Propagation of Uncertainty from Systematic Error

The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this particular...
Sampling Distribution01:12

Sampling Distribution

Given simple random samples of size n from a given population with a measured characteristic such as mean, proportion, or standard deviation for each sample, the probability distribution of all the measured characteristics is called a sampling distribution. How much the statistic varies from one sample to another is known as the sampling variability of a statistic. You typically measure the sampling variability of a statistic by its standard error. The standard error of the mean is an example...
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.
Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
Contaminants and Errors01:16

Contaminants and Errors

Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
Another key consideration is determining the appropriate number of samples required to...

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Related Experiment Video

Updated: Jun 6, 2026

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake
04:46

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake

Published on: September 18, 2018

Sampling variability and uncertainty in total diet studies.

Yoshiki Tsukakoshi1

  • 1National Food Research Institute, 2-1-12 Kannondai, Tsukuba, Ibaraki, Japan. yoshiki.tsukakoshi@gmail.com

The Analyst
|December 2, 2010
PubMed
Summary

Understanding food sampling uncertainty is crucial for total diet studies (TDS). This study found intra-city variance often exceeds inter-city variance, highlighting the importance of sampling design for accurate cadmium concentration analysis.

Related Experiment Videos

Last Updated: Jun 6, 2026

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake
04:46

'Boden Food Plate': Novel Interactive Web-based Method for the Assessment of Dietary Intake

Published on: September 18, 2018

Area of Science:

  • Food safety and nutrition
  • Environmental chemistry
  • Statistical analysis in research

Background:

  • Total diet studies (TDS) are essential for assessing dietary exposure to contaminants.
  • Quantifying uncertainty in TDS is critical for accurate risk assessment.
  • Previous TDS have not fully assessed the uncertainty associated with sampling designs.

Purpose of the Study:

  • To clarify the uncertainty budget in a total diet study (TDS) by dissecting measurement uncertainty.
  • To differentiate between inter-city variance, intra-city variance, and analytical variance in food samples.
  • To evaluate the impact of sampling design, specifically multi-stage sampling, on TDS uncertainty.

Main Methods:

  • Collected TDS samples from 14 cities across Japan.
  • Prepared duplicate food samples from various shops within each city.
  • Measured cadmium concentrations individually to determine intra-city variance and analytical variance.

Main Results:

  • Intra-city variance was consistently higher than inter-city variance across all food groups studied.
  • Cadmium concentrations showed an intra-city correlation, indicating effective sample size is complex.
  • High intra-city variance was particularly noted for bean and potato food groups.

Conclusions:

  • Sampling design significantly impacts the uncertainty budget of total diet studies.
  • Grouping food samples from different shops within the same city enhances result representativeness.
  • Accurate assessment of sampling and analytical uncertainties is vital for robust TDS findings.