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Related Concept Videos

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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Crossover experiments, also called the repeated-measurements design, is a study design in which all experimental units are exposed to all treatments in different periods. Crossover experiments are generally used in psychology, the pharmaceutical industry, agriculture, and medicine.
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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

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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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Scientists always try their best to record measurements with the utmost accuracy and precision. However, sometimes errors do occur. These errors can be random or systematic. Random errors are observed due to the inconsistency or fluctuation in the measurement process, or variations in the quantity itself that is being measured. Such errors fluctuate from being greater than or less than the true value in repeated measurements. Consider a scientist measuring the length of an earthworm using a...
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Correspondence bias, also referred to as the fundamental attribution error, describes the tendency to attribute another person’s behavior to internal characteristics rather than situational influences. This cognitive bias leads individuals to overlook external factors that may be influencing actions, thereby fostering potentially inaccurate assessments of others’ intentions and dispositions.Empirical Evidence for Correspondence BiasResearch has consistently demonstrated the...
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Related Experiment Video

Updated: Feb 5, 2026

Frequency and Distribution of Crossovers in Caenorhabditis elegans Meiosis by SNP Genotyping using Real-time PCR
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Eliminating systematic bias from case-crossover designs.

Xiaoming Wang1, Sukun Wang2, Warren Kindzierski3

  • 1Research Facilitation, Alberta Health Services, Edmonton, Canada.

Statistical Methods in Medical Research
|September 8, 2018
PubMed
Summary

Case-crossover studies can have unavoidable bias. A new calibration method effectively removes this systematic bias, improving estimates for short-term exposure health event research.

Keywords:
Case-crossover designcalibrationpermutationsystematic biasunbiased estimate

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Area of Science:

  • Epidemiology
  • Environmental Health
  • Biostatistics

Background:

  • Case-crossover designs are common for studying short-term exposures and acute health events.
  • Existing methods focus on confounding and bias reduction through reference selection.
  • Systematic bias remains a challenge in these designs.

Purpose of the Study:

  • To explore bias in ambi-directional and time-stratified case-crossover designs.
  • To propose and evaluate a calibration approach for bias elimination.
  • To investigate air pollution and acute myocardial infarction associations.

Main Methods:

  • Simulations using Edmonton air pollution data.
  • Development and application of a calibration technique.
  • Bias, size, and power checks via simulation experiments.

Main Results:

  • Case-crossover designs inherently possess systematic bias.
  • Bias affects effect estimates and significance test p-values.
  • The proposed calibration approach efficiently eliminates systematic bias.

Conclusions:

  • Systematic bias is often unavoidable in case-crossover studies.
  • Uncalibrated designs yield unreliable effect estimates and p-values.
  • The calibration technique offers an efficient solution for bias elimination.