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Related Concept Videos

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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Regression Toward the Mean01:52

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Regression toward the mean (“RTM”) is a phenomenon in which extremely high or low values—for example, and individual’s blood pressure at a particular moment—appear closer to a group’s average upon remeasuring. Although this statistical peculiarity is the result of random error and chance, it has been problematic across various medical, scientific, financial and psychological applications. In particular, RTM, if not taken into account, can interfere when...
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Hindsight Biases01:12

Hindsight Biases

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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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Cause and Effect01:53

Cause and Effect

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While variables are sometimes correlated because one does cause the other, it could also be that some other factor, a confounding variable, is actually causing the systematic movement in our variables of interest. For instance, as sales in ice cream increase, so does the overall rate of crime. Is it possible that indulging in your favorite flavor of ice cream could send you on a crime spree? Or, after committing crime do you think you might decide to treat yourself to a cone?
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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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Bias in Epidemiological Studies01:29

Bias in Epidemiological Studies

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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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Generalized Psychophysiological Interaction PPI Analysis of Memory Related Connectivity in Individuals at Genetic Risk for Alzheimer's Disease
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Pitfalls in Study Interpretation.

Rondi B Gelbard1, Michael W Cripps2

  • 1University of Alabama at Birmingham, Birmingham, Alabama, USA.

Surgical Infections
|July 16, 2021
PubMed
Summary
This summary is machine-generated.

This review highlights common biases in clinical research, such as validity errors and subgroup analyses. Understanding these pitfalls improves the interpretation of study results and therapeutic intervention effectiveness.

Keywords:
E valuefragility indexinfectionsresearch bias

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Area of Science:

  • Clinical Research Methodology
  • Biostatistics
  • Evidence-Based Medicine

Background:

  • Clinical studies aim for unbiased answers to therapeutic questions.
  • Reporting and interpretation pitfalls can compromise evaluation of intervention pragmatism and effectiveness.
  • Awareness of biases is crucial for both study design and result interpretation.

Purpose of the Study:

  • To identify common sources of bias in clinical research.
  • To discuss potential solutions for mitigating these biases.
  • To enhance critical appraisal of scientific literature.

Main Methods:

  • Review of common biases in clinical research.
  • Discussion of internal and external validity issues.
  • Exploration of type 1 and type 2 errors.
  • Analysis of secondary outcome reporting and subgroup analyses.
  • Examination of multiple comparisons.
  • Introduction to fragility index and E-value for assessing study robustness and confounding.

Main Results:

  • Common biases include issues with validity, statistical errors, and inappropriate analysis techniques.
  • The fragility index and E-value offer quantitative measures for assessing study robustness against bias and confounding.
  • Awareness and application of these tools can improve the reliability of research findings.

Conclusions:

  • Understanding common biases is essential for critically evaluating clinical research.
  • Methods like the fragility index and E-value can bolster the validity and interpretability of study conclusions.
  • Improved critical appraisal leads to more reliable evidence-based therapeutic decisions.