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Updated: May 18, 2026

Using Single-Worm Data to Quantify Heterogeneity in Caenorhabditis elegans-Bacterial Interactions

Using Single-Worm Data to Quantify Heterogeneity in Caenorhabditis elegans-Bacterial Interactions

Published on: July 22, 2022

Fitting distributions to microbial contamination data collected with an unequal probability sampling design.

M S Williams¹, E D Ebel, Y Cao

¹Risk Assessment Division, Office of Public Health Science, Food Safety Inspection Service, USDA, Fort Collins, CO 80526, USA. mike.williams@fsis.usda.gov

Journal of Applied Microbiology

|September 19, 2012

Summary

This summary is machine-generated.

Statistical fitting of microbial data often assumes simple random sampling, but this study introduces a weighted maximum likelihood estimation to correct for unequal sampling probabilities, reducing bias in risk assessments.

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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

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Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Microbiota Analysis Using Two-step PCR and Next-generation 16S rRNA Gene Sequencing

Published on: October 15, 2019

Area of Science:

Quantitative microbiology
Statistical modeling
Food safety analysis

Background:

Microbial sampling data analysis commonly uses statistical distribution fitting.
A key assumption is simple random sampling, which is frequently violated in practice.
This violation can lead to biased results in quantitative microbiology and risk assessments.

Purpose of the Study:

To develop a statistical framework for microbial data analysis that accounts for unequal sampling probabilities.
To address biases introduced by the assumption of simple random sampling in microbial data.
To improve the reliability of inferences drawn from microbial sampling data.

Main Methods:

Development of a weighted maximum likelihood estimation (WMLE) framework.
Application of WMLE to microbiological samples collected with unequal selection probabilities.
Demonstration using two case studies involving food sample collection during processing.

Main Results:

The proposed weighted maximum likelihood estimation framework is suitable for microbiological samples with unequal selection probabilities.
Biases resulting from the assumption of simple random sampling were quantified in the provided examples.
The study highlights the significant impact of ignoring unequal sampling probabilities on statistical inferences.

Conclusions:

Failure to account for unequal sample weighting can introduce substantial bias into data analysis.
The proposed methodology mitigates bias in microbial data analysis, enhancing the reliability of risk assessments.
This approach facilitates more accurate comparisons and data integration across different studies.