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Related Concept Videos

Heuristics01:21

Heuristics

Heuristics are problem-solving strategies that use mental shortcuts to simplify decision-making. Unlike algorithms, which must be followed precisely to achieve a correct result, heuristics offer a general problem-solving framework. They save time and energy but can sometimes lead to less rational decisions.
People often rely on heuristics when faced with an overload of information, limited time, low importance of the decision, limited information, or when a heuristic readily comes to mind. For...
The Availability Heuristic01:08

The Availability Heuristic

A heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. Different types of heuristics are used in different types of situations, and the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):
Reason and Intuition01:37

Reason and Intuition

The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the brain can only use...
Decision Making01:20

Decision Making

Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
Automatic decision-making is fast, intuitive, and relies on gut feelings...
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
The Representativeness Heuristic02:13

The Representativeness Heuristic

The representative heuristic describes a biased way of thinking, in which you unintentionally stereotype someone or something. For example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

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The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
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The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients

Published on: June 12, 2020

When decision heuristics and science collide.

Erica C Yu1, Amber M Sprenger, Rick P Thomas

  • 1Department of Psychology, University of Maryland, College Park, MD, 20742, USA.

Psychonomic Bulletin & Review
|September 5, 2013
PubMed
Summary
This summary is machine-generated.

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Last Updated: May 8, 2026

The Adventures of Fundi Intervention Based on the Cognitive and Emotional Processing in Attention Deficit Hyperactive Disorder Patients
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Area of Science:

  • Scientific methodology
  • Research practices
  • Data analysis

Background:

  • Ongoing debate on null-hypothesis significance testing (NHST) vs. Bayesian data analysis.
  • Concerns regarding researcher degrees of freedom (RDF) and their impact on scientific conclusions.
  • Lack of understanding regarding scientists' decision-making processes during research.

Purpose of the Study:

  • To investigate how scientists make decisions during research.
  • To assess the impact of these decisions on scientific conclusions.
  • To examine the influence of data collection heuristics on statistical inference.

Main Methods:

  • Practicing scientists collected data in a simulated research environment.
  • Monte Carlo simulations were used to model the effects of data collection heuristics.
  • Analysis focused on biases in effect size estimation, Bayes factors, and error rates.

Main Results:

  • Scientists' data collection heuristics sometimes deviate from prescribed methodologies.
  • Heuristics based on p-values introduce biases in effect size and Bayes factor estimates.
  • Both false-positive and false-negative rates increase due to these heuristics.
  • Bayesian data collection methods do not inherently eliminate these biases.

Conclusions:

  • Scientific research is a behavioral process susceptible to biases.
  • Decision-making during data collection can significantly impact statistical descriptions and inferences.
  • Biases transcend specific statistical frameworks like NHST or Bayesian analysis.