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Related Concept Videos

Bias01:22

Bias

7.8K
Bias refers to any tendency that prevents a question from being considered unprejudiced. In research, bias occurs when one outcome or answer is selected or encouraged over others in sampling or testing. Bias can occur during any research phase, including study design, data collection, analysis, and publication.
In statistics, a sampling bias is created when a sample is collected from a population, and some members of the population are not as likely to be chosen as others (remember, each member...
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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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Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

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When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
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Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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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...
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Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches01:23

Types of Biopharmaceutical Studies: Controlled and Non-Controlled Approaches

508
Biopharmaceutical studies constitute a vital field aiming to enhance drug delivery methods and refine therapeutic approaches, drawing upon diverse interdisciplinary knowledge. In research methodologies, the choice between controlled and non-controlled studies significantly influences the study's reliability and accuracy.
Non-controlled studies, commonly employed for initial exploration, lack a control group, rendering them susceptible to biases and external influences. In contrast,...
508
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

4.3K
Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Related Experiment Video

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Comparing Bibliometric Analysis Using PubMed, Scopus, and Web of Science Databases
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On the Need for Quantitative Bias Analysis in the Peer-Review Process.

Matthew P Fox, Timothy L Lash

    American Journal of Epidemiology
    |April 22, 2017
    PubMed
    Summary

    Quantitative bias analysis can improve the peer-review process in epidemiology. Incorporating this method strengthens research validity and informs public health interventions by quantifying potential errors.

    Keywords:
    biascausal inferenceerrorpeer reviewquantitative bias analysissystematic error

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    Area of Science:

    • Epidemiology and Public Health
    • Scientific Research Methodology

    Background:

    • Peer review is critical for validating epidemiological research and informing public health interventions.
    • Existing peer-review systems face noted problems, with few proposed solutions for improvement.
    • Quantitative bias analysis is a key method for assessing systematic error in epidemiological studies.

    Purpose of the Study:

    • To advocate for the integration of quantitative bias analysis into the peer-review process for epidemiological research.
    • To demonstrate the utility of quantitative bias analysis for researchers, reviewers, and editors.
    • To enhance the rigor and reliability of evidence used in public health and medical decision-making.

    Main Methods:

    • The commentary discusses the application of quantitative bias analysis across the research lifecycle: design, conduct, presentation, and interpretation.
    • It highlights how quantitative bias analysis can be utilized by various stakeholders, including authors, reviewers, and editors.
    • The text emphasizes shifting from speculative discussions of error to quantified assessments of bias impact.

    Main Results:

    • Incorporating quantitative bias analysis can significantly strengthen the peer-review process for epidemiological research.
    • This approach aids editors in identifying critical areas for methodological improvement in submitted manuscripts.
    • It facilitates more informed discussions regarding the impact of systematic errors on research findings.

    Conclusions:

    • Adoption of quantitative bias analysis by reviewers and editors can elevate the quality of published epidemiological research.
    • This method helps mitigate unnecessary rejections and improves the interpretation of study results.
    • Quantitative bias analysis provides a robust framework for strengthening evidence-based public health and medical practices.