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Related Concept Videos

Bias01:22

Bias

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...
Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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:
Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data01:16

Statistical Inference Techniques in Hypothesis Testing: Parametric Versus Nonparametric Data

Statistical inference techniques, paramount in hypothesis testing, differentiate into two broad categories: parametric and nonparametric statistics.
Parametric statistics, as the name suggests, assumes that data follow a specific distribution, often a normal distribution. This assumption enables robust hypothesis testing and estimation. Parametric methods, like the Student's t-test or Goodness-of-fit test, are frequently employed in biostatistics due to their robustness. For instance, comparing...
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

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...
Weighted Mean00:57

Weighted Mean

While taking the arithmetic, geometric, or harmonic mean of a sample data set, equal importance is assigned to all the data points. However, all the values may not always be equally important in some data sets. An intrinsic bias might make it more important to give more weightage to specific values over others.
For example, consider the number of goals scored in the matches of a tournament. While computing the average number of goals scored in the tournament, it may be more important to...
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

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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Related Experiment Video

Updated: Jun 19, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Reporting bias when using real data sets to analyze classification performance.

Mohammadmahdi R Yousefi1, Jianping Hua, Chao Sima

  • 1Department of Electrical and Computer Engineering, Texas A&M University, College Station, TX 77843, USA.

Bioinformatics (Oxford, England)
|October 23, 2009
PubMed
Summary
This summary is machine-generated.

Reporting only the best results from classification studies leads to biased performance metrics. Simulations show this reporting bias is significant, even when comparing top-performing datasets. Researchers should report all results for real data studies.

Related Experiment Videos

Last Updated: Jun 19, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

Area of Science:

  • Machine Learning
  • Bioinformatics
  • Statistical Modeling

Background:

  • Authors commonly propose new classification rules and demonstrate performance on high-dimensional, small-sample real datasets.
  • Variability in feature selection and error estimation leads to imprecise performance reporting.
  • Reporting only the best test results introduces bias relative to overall procedure performance.

Purpose of the Study:

  • To characterize and quantify reporting bias in classification performance.
  • To evaluate bias across different classification rules and feature selection methods.
  • To provide recommendations for more reliable reporting practices.

Main Methods:

  • Conducted a large simulation study using modeled and real data.
  • Computed reporting bias statistics for various scenarios.
  • Tested linear discriminant analysis (LDA) and 3-nearest-neighbor (3NN) classification rules.
  • Evaluated filter (t-test) and wrapper (sequential forward search) feature selection methods.

Main Results:

  • Reporting bias was generally large, often overriding significant performance differentials.
  • Bias was quantified as a function of the number of samples tested.
  • Results were consistent across different classification rules and feature selection techniques.
  • Bias was observed when reporting the best or second-best performing dataset.

Conclusions:

  • There is a substantial reporting bias when only top-performing datasets are presented.
  • A centralized database of datasets is recommended for comprehensive evaluation.
  • For studies using real data, results should be reported for all datasets tested.