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The Anchoring-and-Adjustment Heuristic01:25

The Anchoring-and-Adjustment Heuristic

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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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Confirmation Biases01:31

Confirmation Biases

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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?
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Hindsight Biases01:12

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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? 
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Bias01:22

Bias

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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.
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...
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Adjusting a Traverse01:12

Adjusting a Traverse

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In the site survey of a four-sided traverse, internal angles are essential to ensure geometric accuracy. The survey revealed that the sum of the measured internal angles was 359 degrees and 48 minutes, which is 12 minutes less than the expected 360 degrees. This discrepancy signals an error likely arising from measurement inaccuracies during the fieldwork.To rectify this error, the adjustment process involved distributing the 12-minute shortfall equally across the four internal angles. By...
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Directing Effect of Substituents: meta-Directing Groups01:09

Directing Effect of Substituents: meta-Directing Groups

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Substituents on the benzene ring that direct an incoming electrophile to undergo substitution at the meta position are called meta directors. All meta directors either have a positive charge on the atom directly bonded to the ring or a partial positive charge. These groups function by withdrawing electrons from the ring through inductive and resonance effects. Consider the carbocation intermediates formed upon the addition of an electrophile on nitrobenzene at the...
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Video Experimental Relacionado

Updated: Feb 5, 2026

Meta-Analysis of the Effectiveness and Safety of Shugan Jieyu Capsules for the Treatment of Insomnia
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Flujo de trabajo bayesiano para el modelo de ajuste de sesgos en metaanálisis

Juyoung Jung1, Ariel M Aloe1

  • 1Educational Measurement and Statistics, https://ror.org/036jqmy94The University of Iowa, United States.

Research synthesis methods
|February 4, 2026
PubMed
Resumen
Este resumen es generado por máquina.

Este estudio presenta un flujo de trabajo bayesiano para el ajuste de sesgos complejos en metaanálisis. El flujo de trabajo resalta la sensibilidad de las priors, mostrando que los modelos de sesgos producen intervalos conservadores, cruciales para una síntesis de evidencia robusta.

Palabras clave:
metaanálisis bayesianoflujo de trabajo bayesianoajuste de sesgosvalidación de modelosriesgo de sesgo

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Área de la Ciencia:

  • Estadística
  • Bioestadística
  • Síntesis de Evidencia

Sus antecedentes:

  • Los modelos jerárquicos bayesianos son valiosos para el ajuste de sesgos en metaanálisis.
  • Su complejidad y sensibilidad a las priors requieren marcos de aplicación sistemáticos.

Objetivo del estudio:

  • Demostrar un flujo de trabajo bayesiano para aplicar y evaluar modelos de ajuste de sesgos en metaanálisis.
  • Comparar un modelo estándar de efectos aleatorios con un modelo de ajuste de sesgos.

Principales métodos:

  • Se aplicó un flujo de trabajo bayesiano a un conjunto de datos del mundo real y a un estudio de simulación.
  • Se comparó un modelo estándar de efectos aleatorios con un modelo de ajuste de sesgos.
  • Se evaluó el rendimiento del modelo utilizando el criterio de información ampliamente aplicable y los intervalos creíbles.

Principales resultados:

  • Los resultados mostraron alta sensibilidad a la prior sobre la probabilidad de sesgo.
  • El modelo de efectos aleatorios tuvo una mejor precisión predictiva, mientras que el modelo de ajuste de sesgos produjo intervalos creíbles más amplios y conservadores.
  • Las simulaciones confirmaron la recuperación de parámetros con priors bien especificadas.

Conclusiones:

  • El flujo de trabajo bayesiano proporciona un enfoque principista para diagnosticar las sensibilidades del modelo en metaanálisis.
  • Asegura la aplicación transparente y robusta de modelos complejos de ajuste de sesgos en la síntesis de evidencia.