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Related Concept Videos

Bias01:22

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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.
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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.
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α,β-Unsaturated carbonyl compounds with two electrophilic sites, the carbonyl carbon, and the β carbon, are susceptible to nucleophilic attack via two modes: conjugate or 1,4-addition and direct or 1,2-addition.
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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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The Anchoring-and-Adjustment Heuristic01:25

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In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. However, sometimes, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the...
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A complete procedure for testing a claim about a population proportion is provided here.
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More is Better: English Language Statistics are Biased Toward Addition.

Bodo Winter1, Martin H Fischer2, Christoph Scheepers3

  • 1Department of English Language & Linguistics, University of Birmingham.

Cognitive Science
|April 5, 2023
PubMed
Summary

Humans exhibit an addition bias, favoring additive changes over subtractive ones. This bias is evident in language, decision-making, and even artificial intelligence models.

Keywords:
AdditionHeuristics and biasesLatent semantic analysisSubtractionSubtraction neglectWord frequency

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Area of Science:

  • Cognitive Psychology
  • Linguistics
  • Artificial Intelligence

Background:

  • Humans possess a drive for creation and improvement.
  • Subtractive changes are often overlooked in favor of additive ones.
  • This tendency is exemplified by academic paper reviews suggesting additions over deletions.

Purpose of the Study:

  • To investigate the systematic presence of an addition bias in human cognition and language.
  • To explore whether this bias extends to artificial intelligence models.
  • To understand the implications for cognitive biases and decision-making.

Main Methods:

  • Analysis of English language statistics, focusing on word frequency and binomial expressions.
  • Examination of distributional semantics of verbs related to change, addition, and subtraction.
  • Evaluation of large language models (e.g., GPT-3) for additive bias.
  • Connotation analysis of addition vs. subtraction related words.

Main Results:

  • Words related to addition are more frequent than those for subtraction.
  • Addition-related words precede subtraction-related words in binomial expressions.
  • Verbs of change show semantic overlap biased towards addition.
  • Addition words carry more positive connotations than subtraction words.
  • Large language models demonstrate a similar addition bias.

Conclusions:

  • A pervasive addition bias exists in human cognition and language.
  • This bias is reflected in linguistic patterns and semantic structures.
  • Artificial intelligence models trained on human language data inherit this bias.
  • Understanding addition bias is crucial for research on decision-making and cognitive biases.