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Related Concept Videos

Data Collection by Observations01:08

Data Collection by Observations

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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Observational Studies01:11

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The actor-observer effect, a cognitive bias closely linked to the fundamental attribution error, refers to the tendency for individuals to attribute their behavior to external, situational factors while explaining others’ behavior in terms of internal, dispositional traits. This asymmetry in attribution significantly influences social perception and judgment.Cognitive Mechanisms Behind the EffectTwo primary psychological mechanisms contribute to the actor-observer effect: differences in...
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Observational Learning01:12

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Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
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How Data are Classified: Numerical Data00:59

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Data that are countable or measurable in specific units are called numerical or quantitative data. Quantitative data are always numbers. Quantitative data are the result of counting or measuring the attributes of a population. Amount of money, pulse rate, weight, number of people living in a town, and number of students who opt for statistics are examples of quantitative data.
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Related Experiment Video

Updated: Jan 25, 2026

Corticospinal Excitability Modulation During Action Observation
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Counterintuitive results from observational data: a case study and discussion.

Erik Doty1, David J Stone2,3, Ned McCague3,4

  • 1Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA.

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|May 8, 2019
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Higher patient pain after cardiac surgery was unexpectedly linked to better survival and shorter hospital stays. This counterintuitive finding in critical care data warrants careful examination and further research into its implications.

Keywords:
length of staymortalitypain

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Area of Science:

  • Critical care medicine
  • Medical data analysis
  • Clinical outcomes research

Background:

  • Counterintuitive findings in medical data present challenges to existing knowledge.
  • Analyzing unexpected results is crucial for advancing clinical understanding.
  • The increasing volume of big data in healthcare necessitates frameworks for evaluating surprising outcomes.

Purpose of the Study:

  • To explore the phenomenon of counterintuitive data using a case study.
  • To develop a framework for approaching and analyzing unexpected research findings.
  • To investigate the association between perceived pain and patient outcomes after cardiac surgery.

Main Methods:

  • Retrospective analysis of a cohort of 844 coronary artery bypass graft (CABG) patients.
  • Utilized the Medical Information Mart for Intensive Care-III (MIMIC-III) database.
  • Employed regression analysis to examine the relationship between pain levels and outcomes.

Main Results:

  • Increased perceived pain in the intensive care unit (ICU) was significantly associated with reduced 30-day and 1-year mortality.
  • Higher pain levels correlated with a shorter hospital length of stay (LOS).
  • A one-point increase in pain was linked to a 0.457 odds reduction in 30-day mortality and a 0.916-day decrease in hospital LOS.

Conclusions:

  • The association between increased pain and improved outcomes is clinically counterintuitive.
  • Reliability of such unexpected findings must be rigorously examined.
  • Counterintuitive results may indicate gaps in current medical knowledge or prompt new research directions.