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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...

Blinding

Blinding

Blinding is a commonly used method of not telling participants which treatment a subject is receiving. Blinding is a critical part of a randomized control trial or RCT. It reduces the bias that affects the results. In an RCT, blinding is used in the form of a placebo. A placebo effect occurs when untreated subjects falsely believe they have received the treatment and report improved symptoms. A placebo or a dummy treatment is administered to subjects to negate the bias caused by such an effect.

Blind Procedures

Blind Procedures

Ideally, the people who observe and record the children’s behavior are unaware of who was assigned to the experimental or control group, in order to control for experimenter bias. Experimenter bias refers to the possibility that a researcher’s expectations might skew the results of the study. Remember, conducting an experiment requires a lot of planning, and the people involved in the research project have a vested interest in supporting their hypotheses. If the observers knew which...

Strategies for Assessing and Addressing Confounding

Strategies for Assessing and Addressing Confounding

Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...

Stereotypes, Prejudice, and Discrimination

Stereotypes, Prejudice, and Discrimination

Humans are very diverse and although we share many similarities, we also have many differences. The social groups we belong to help form our identities (Tajfel, 1974). These differences may be difficult for some people to reconcile, which may lead to prejudice toward people who are different. Prejudice is a negative attitude and feeling toward an individual based solely on one’s membership in a particular social group (Allport, 1954; Brown, 2010). Prejudice is common against people who...

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:

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Related Experiment Video

Updated: Aug 30, 2025

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

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

Published on: January 8, 2020

The Future Strikes Back: Using Future Treatments to Detect and Reduce Hidden Bias.

Felix Elwert¹, Fabian T Pfeffer²

¹University of Wisconsin-Madison, WI, USA.

Sociological Methods & Research

|August 26, 2022

Summary

This study demonstrates that using future treatment values in regression analysis can effectively mitigate omitted variable bias. This novel approach aids in detecting and reducing unobserved confounding for more accurate causal inference.

Keywords:

bias causal inference confounding directed acyclic graphs future treatments

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Last Updated: Aug 30, 2025

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Area of Science:

Econometrics
Causal Inference
Statistical Modeling

Background:

Conventional regression analysis often advises against controlling for postoutcome variables.
Omitted variable bias (unobserved confounding) remains a significant challenge in causal inference.

Purpose of the Study:

To investigate the utility of controlling for future treatment values to address omitted variable bias.
To introduce and evaluate new methods for bias reduction and detection using future treatment information.

Main Methods:

Developed a new approach to control for future treatment values in regression analysis.
Introduced a nonparametric test to detect hidden bias using future treatments.
Empirically illustrated the methods with data on parental income and children's educational attainment.

Main Results:

The proposed method strictly reduces omitted variable bias.
Existing approaches may inadvertently increase bias.
A new test effectively detects hidden bias, even when estimation methods fail.

Conclusions:

Controlling for future treatment values is a productive strategy to combat omitted variable bias.
The developed methods offer advancements in detecting and reducing unobserved confounding in observational studies.