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Related Concept Videos

Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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Biases can arise at various stages of research, from study design and data collection to analysis and interpretation. Recognizing and addressing these biases is essential to ensure the validity and reliability of epidemiological findings.Broadly speaking, biases in epidemiology fall into three main categories: selection bias, information bias, and confounding. A more detailed description of possible biases is:  
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Introduction to Epidemiology01:26

Introduction to Epidemiology

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Epidemiology, known as the cornerstone of public health, involves studying the distribution and determinants of health-related events in defined populations and applying these insights to control health issues. This is essential for understanding how diseases spread, identifying populations at greater risk, and implementing measures to control or prevent outbreaks. Epidemiology addresses not only infectious diseases but also non-communicable conditions like cancer and cardiovascular disease,...
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Convenience Sampling Method00:55

Convenience Sampling Method

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
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Contaminants and Errors01:16

Contaminants and Errors

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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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Sampling Plans01:23

Sampling Plans

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Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
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Sampling Methods: Overview01:06

Sampling Methods: Overview

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A sample refers to a smaller subset representative of a larger population. In analytical chemistry, studying or analyzing an entire population is often impractical or impossible. Therefore, samples are used to draw inferences and generalize the whole population. The sampling method selects individuals or items from a population to create a sample. Standard sampling methods include random, judgemental, systematic, stratified, and cluster sampling. 
In analytical chemistry, the choice of...
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Related Experiment Video

Updated: Mar 22, 2026

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
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Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India

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Sampling in epidemiological research: issues, hazards and pitfalls.

Stephen Tyrer1, Bob Heyman2

  • 1Newcastle University, UK.

Bjpsych Bulletin
|April 19, 2016
PubMed
Summary

Obtaining a representative sample for surveys is challenging. Using convenience samples can introduce bias, making random sampling crucial for valuable epidemiological research.

Area of Science:

  • Epidemiology
  • Survey Methodology
  • Biostatistics

Background:

  • Surveys face challenges in obtaining representative samples.
  • Convenience samples, often recruited via text or email, are easier to obtain but introduce sampling bias.
  • Non-probability samples may have utility but are inadequate for rigorous epidemiological studies.

Purpose of the Study:

  • To describe probability and non-probability sampling methods.
  • To illustrate the difficulties in performing accurate epidemiological research.
  • To suggest solutions for improving sampling accuracy in epidemiology.

Main Methods:

  • Discussion of probability sampling techniques (e.g., random sampling).
  • Explanation of non-probability sampling techniques (e.g., convenience sampling).

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Swabbing the Urban Environment - A Pipeline for Sampling and Detection of SARS-CoV-2 From Environmental Reservoirs

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Related Experiment Videos

Last Updated: Mar 22, 2026

Visualizing Field Data Collection Procedures of Exposure and Biomarker Assessments for the Household Air Pollution Intervention Network Trial in India
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  • Analysis of sampling bias and its impact on research validity.

    • Main Results:

    • Convenience sampling leads to non-representative samples and introduces bias.
    • Random sampling is essential for epidemiological research to yield valid results.
    • High survey completion rates in random samples enhance representativeness.

    Conclusions:

    • Accurate epidemiological research necessitates the use of probability sampling methods.
    • Addressing sampling bias is critical for the integrity of survey findings.
    • Careful consideration of sampling strategies can improve the quality of epidemiological data.