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Related Concept Videos

Bias01:22

Bias

4.3K
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...
4.3K
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

339
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:  
339
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

6.6K
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...
6.6K
Regression Toward the Mean01:52

Regression Toward the Mean

6.3K
Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
6.3K
Systematic Error: Methodological and Sampling Errors01:15

Systematic Error: Methodological and Sampling Errors

1.5K
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...
1.5K
Instrument Calibration01:12

Instrument Calibration

217
Instrument calibration is essential for ensuring that instruments produce accurate and consistent results. It is vital in manufacturing, healthcare, testing laboratories, and scientific research. Calibration processes are specific to each instrument and help enhance data accuracy. Each instrument has a unique calibration process tailored to its design and function to improve data accuracy.
Analytical Balance Calibration
An analytical balance measures mass and requires regular calibration to...
217

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Related Experiment Video

Updated: Jul 15, 2025

Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method
08:53

Strand-Specific Analysis of Proteins at Replicating DNA Strands by Enrichment and Sequencing of Protein-Associated Nascent DNA Method

Published on: May 2, 2025

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Measuring, visualizing and diagnosing reference bias with biastools.

Mao-Jan Lin1, Sheila Iyer1, Nae-Chyun Chen1

  • 1Department of Computer Science, Johns Hopkins University.

Biorxiv : the Preprint Server for Biology
|September 25, 2023
PubMed
Summary
This summary is machine-generated.

Biastools is a new method to measure reference bias in bioinformatics. It reveals that inclusive graph genomes and end-to-end alignment reduce bias, especially for indels.

Keywords:
pangenomicsreference biassequence alignment

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Area of Science:

  • Bioinformatics
  • Genomics
  • Computational Biology

Background:

  • Bioinformatics methods aim to minimize reference bias, but a comprehensive measurement tool was lacking.
  • Reference bias can impact variant calling accuracy and downstream genomic analyses.
  • Existing methods do not systematically quantify reference bias across different scenarios.

Conclusions:

  • Biastools provides a comprehensive framework for measuring and understanding reference bias in genomic data.
  • The findings highlight the importance of genome representation (e.g., graph genomes) and alignment techniques in mitigating reference bias.
  • The study underscores the benefits of advanced references like T2T in reducing systematic biases in large-scale genomic studies.