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Related Concept Videos

Systematic Error: Methodological and Sampling Errors01:15

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In the case of systematic errors, the sources can be identified, and the errors can be subsequently minimized by addressing these sources. According to the source, systematic errors can be divided into sampling, instrumental, methodological, and personal errors.
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According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
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Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
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The margin of error is also called the maximum error of an estimate. The margin of error is the maximum possible or expected difference between the observed sample parameter value and the actual population parameter value. For proportion, it is the maximum difference between the value of sample proportion obtained from the data and the true value of population proportion. As the true value of the population parameter is not known, the margin of error is calculated using the sample statistic.
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The sampling variability of a statistic is defined as how much the statistic varies from one sample to another. The sampling variability of a statistic is typically measured by measuring its standard error.
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Effective sample preparation is crucial for accurate and reliable laboratory analysis. During this process, two significant sources of error can arise: concentration bias from improper sample splitting and contamination caused by methods used to reduce particle size, such as grinding or homogenization. Identifying and minimizing these potential errors is crucial to ensuring the validity of the analysis.
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Updated: Feb 14, 2026

Clinical Imaging of Microwave Mammography
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Errors in Mammography Cannot be Solved Through Technology Alone

Ernest Usang Ekpo1, Maram Alakhras, Patrick Brennan

  • 1Discipline of Medical Radiation Sciences, Faculty of Health Sciences, University of Sydney, Sydney, Australia.

Asian Pacific Journal of Cancer Prevention : APJCP
|February 27, 2018
PubMed
Summary
This summary is machine-generated.

Mammography screening for breast cancer has persistent high error rates, including missed cancers and unnecessary recalls. Understanding factors causing these errors is key to improving diagnostic accuracy.

Keywords:
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Area of Science:

  • Radiology
  • Oncology
  • Medical Imaging

Background:

  • Mammography is a primary breast cancer screening tool, but suffers from significant false negatives and false positives.
  • Error rates persist despite technological advancements, leading to missed diagnoses and unnecessary patient anxiety and procedures.

Purpose of the Study:

  • To review current knowledge on mammography error rates.
  • To explore factors contributing to diagnostic errors in mammography.
  • To present potential solutions for enhancing diagnostic efficacy.

Main Methods:

  • Literature review of studies on mammography error rates.
  • Analysis of causal factors including human, technical, patient, and lesion characteristics.
  • Exploration of technological and educational interventions.

Main Results:

  • Radiologists miss 10-30% of cancers; 80% of recalls are benign, and 40% of biopsied lesions are benign.
  • Missed cancers are often visible but overlooked or misclassified.
  • Human factors (search, perception, decision-making), technical issues, patient characteristics (size, density), and lesion types (architectural distortion, triple-negative) contribute to errors.

Conclusions:

  • A comprehensive understanding of error-causal agents is crucial.
  • Technological and educational interventions, including performance audits and feedback, can improve diagnostic accuracy.
  • Optimizing mammography efficacy benefits millions of women screened annually.