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Bias

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 Studies

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:

Hindsight Biases

Hindsight Biases

Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Can you relate this to the phrase "Hindsight is 20/20" now?

Motivational Bias

Motivational Bias

Cognitive bias results from limitations in thinking and information processing, leading to systematic errors in judgment. Conversely, motivational bias stems from personal desires or emotions, causing distortions in perception to align with self-interest. Motivational bias influences how individuals perceive and attribute causes to events, often shaped by personal needs, goals, and self-esteem preservation. This bias can distort judgment, leading to inaccurate assessments of success, failure,...

Confirmation Biases

Confirmation Biases

The confirmation bias is the tendency to focus on information that confirms our existing beliefs and ignore information that is inconsistent with our expectations. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Have you ever fallen prey to the confirmation bias, either as the source or target of such bias?

Systematic Error: Methodological and Sampling Errors

Systematic Error: Methodological and Sampling Errors

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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Related Experiment Video

Updated: Mar 30, 2026

Assessment of Mouse Judgment Bias through an Olfactory Digging Task

Assessment of Mouse Judgment Bias through an Olfactory Digging Task

Published on: March 4, 2022

Are all biases missing data problems?

Chanelle J Howe¹, Lauren E Cain², Joseph W Hogan³

¹Department of Epidemiology, Center for Population Health and Clinical Epidemiology, Brown University School of Public Health, 121 South Main Street, Providence, Rhode Island 02912 (, , chanelle_howe@brown.edu ).

Current Epidemiology Reports

|November 18, 2015

Summary

This summary is machine-generated.

Epidemiologic studies often face bias from confounding, selection, and measurement error. This research reframes these biases as missing data problems, offering solutions for more accurate causal effect estimation.

Keywords:

Confounding bias Measurement bias Missing data Selection bias

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Last Updated: Mar 30, 2026

Assessment of Mouse Judgment Bias through an Olfactory Digging Task

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Published on: March 4, 2022

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index

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Published on: January 8, 2020

Area of Science:

Epidemiology
Biostatistics

Background:

Estimating causal effects is crucial in epidemiology.
Traditional threats include confounding, selection bias, and measurement error.
These biases are often viewed as distinct issues.

Purpose of the Study:

To reframe confounding, selection, and measurement bias as missing data problems.
To review methods for mitigating these biases using missing data solutions.
To link bias-reducing method assumptions to missing data frameworks like MCAR and MAR.

Main Methods:

Characterizing common epidemiologic biases as missing data issues.
Reviewing statistical methods for addressing missing data.
Connecting assumptions of bias reduction techniques to missing completely at random (MCAR) and missing at random (MAR) criteria.

Main Results:

Demonstrates how confounding, selection, and measurement error manifest as missing data.
Identifies specific missing data techniques applicable to bias reduction.
Establishes a framework for evaluating assumptions underlying these methods.

Conclusions:

Viewing biases as missing data provides a unified approach to causal inference.
Missing data solutions offer powerful tools for addressing traditional epidemiologic biases.
Understanding the link to MCAR/MAR assumptions is key for valid causal effect estimation.