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Related Concept Videos

Bias01:22

Bias

6.6K
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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Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

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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...
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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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Accuracy and Errors in Hypothesis Testing01:13

Accuracy and Errors in Hypothesis Testing

419
Hypothesis testing is a fundamental statistical tool that begins with the assumption that the null hypothesis H0 is true. During this process, two types of errors can occur: Type I and Type II. A Type I error refers to the incorrect rejection of a true null hypothesis, while a Type II error involves the failure to reject a false null hypothesis.
In hypothesis testing, the probability of making a Type I error, denoted as α, is commonly set at 0.05. This significance level indicates a 5%...
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Random and Systematic Errors01:20

Random and Systematic Errors

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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...
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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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Basics of Multivariate Analysis in Neuroimaging Data
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Bias Analysis Gone Bad.

Timothy L Lash, Thomas P Ahern, Lindsay J Collin

    American Journal of Epidemiology
    |March 29, 2021
    PubMed
    Summary
    This summary is machine-generated.

    Quantitative bias analysis in epidemiology is underutilized, leading to potential misuse. Improving methods and adhering to best practices can enhance research quality and prevent misleading results.

    Keywords:
    epidemiologic biasepidemiologic methodsquantitative bias analysis

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    Area of Science:

    • Epidemiology
    • Biostatistics
    • Scientific Research Integrity

    Background:

    • Quantitative bias analysis is crucial for assessing systematic errors in epidemiologic research.
    • Despite available methods and guidance, its application in published research remains infrequent.
    • Lack of familiarity may lead to unintentional or intentional misuse of bias analysis.

    Purpose of the Study:

    • To evaluate the current state of quantitative bias analysis in epidemiologic research.
    • To identify and illustrate common shortcomings in bias analysis through case examples.
    • To provide recommendations for improving the quality and transparency of bias analysis.

    Main Methods:

    • Reviewed three published epidemiologic studies with suboptimal bias analysis.
    • Assessed each bias analysis against established good practice guidelines.
    • Described potential improvements for the bias analysis and interpretation of findings.

    Main Results:

    • Identified common flaws including lack of clear bias models, computed examples, and computing code.
    • Noted poor parameter selection and insufficient exploration of uncertainty in bias models.
    • Highlighted how improved bias analysis could have strengthened the original research findings.

    Conclusions:

    • Suboptimal bias analysis can compromise the integrity of epidemiologic research findings.
    • Adherence to good practices in bias analysis is essential for improving quality and preventing manipulation.
    • Establishing community expectations for bias analysis presentation and interpretation is needed.