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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...

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?

First Impression

First Impression

First impressions play a crucial role in social perception, shaping how individuals assess others in professional, academic, and interpersonal contexts. Psychological research highlights the significance of cognitive biases, such as the primacy and recency effects, which influence how people interpret and recall information.The Primacy Effect and Cognitive AnchoringThe primacy effect describes the tendency for initial information to impact judgment disproportionately. When individuals encounter...

Cause and Effect

Cause and Effect

While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?

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?

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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A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

A Psychophysics Paradigm for the Collection and Analysis of Similarity Judgments

Published on: March 1, 2022

Sequential biases in accumulating evidence.

Elena Kulinskaya¹, Richard Huggins², Samson Henry Dogo³

¹School of Computing Sciences, University of East Anglia, Norwich, NR4 7TJ, UK. e.kulinskaya@uea.ac.uk.

Research Synthesis Methods

|December 3, 2015

Summary

This summary is machine-generated.

Clinical trial decisions based on accumulating evidence can introduce sequential decision and design biases. Minimizing these biases is crucial for accurate evidence synthesis and reliable scientific development.

Keywords:

accumulating evidence cumulative meta-analysis sequential bias sequential meta-analysis

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Area of Science:

Biostatistics
Clinical Trial Methodology
Evidence Synthesis

Background:

Clinical trials commonly use interim results to guide subsequent phases and trial design.
This practice, while seemingly efficient, can introduce systematic errors into the overall evidence base.

Purpose of the Study:

To identify and define new sources of bias arising from sequential decision-making in clinical trials.
To analyze the impact of these biases on meta-analysis results under different statistical models.

Main Methods:

Analytical derivations and simulation studies were employed to investigate the identified biases.
Both fixed-effect and random-effects meta-analysis models were considered.

Main Results:

Two novel biases, 'sequential decision bias' and 'sequential design bias', were identified and characterized.
Simulations confirmed that these biases are present in both fixed-effect and random-effects meta-analyses.
The magnitude of sequential biases was observed to increase with greater statistical heterogeneity.

Conclusions:

Sequential decision-making in clinical trials can lead to biased evidence synthesis.
Minimizing sequential biases is a critical area for future research to ensure robust evidence-based scientific advancement.