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Related Concept Videos

Random and Systematic Errors01:20

Random and Systematic Errors

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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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Scientists typically make repeated measurements of a quantity to ensure the quality of their findings and to evaluate both the precision and the accuracy of their results. Measurements are said to be precise if they yield very similar results when repeated in the same manner. A measurement is considered accurate if it yields a result that is very close to the true or the accepted value. Precise values agree with each other; accurate values agree with a true value. 
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An experiment often consists of more than a single step. In this case, measurements at each step give rise to uncertainty. Because the measurements occur in successive steps, the uncertainty in one step necessarily contributes to that in the subsequent step. As we perform statistical analysis on these types of experiments, we must learn to account for the propagation of uncertainty from one step to the next. The propagation of uncertainty depends on the type of arithmetic operation performed on...
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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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Updated: Mar 17, 2026

An Open Source Technology Platform to Manufacture Hydrogel-Based 3D Culture Models in an Automated and Standardized Fashion
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Thou Shalt Be Reproducible! A Technology Perspective.

Patrick Mair1

  • 1Department of Psychology, Harvard University Cambridge, MA, USA.

Frontiers in Psychology
|July 30, 2016
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Summary

This study highlights open-source technologies for enhancing reproducibility in psychological research. Emerging researchers should adopt these tools for transparent data archiving, reproducible statistical analysis using R, and dynamic manuscript generation.

Keywords:
LATEXRR Markdowndata archivingknitropen sourcereproducibility

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Area of Science:

  • Psychology
  • Computational Science
  • Research Methodology

Background:

  • Reproducibility is a critical challenge in psychological research.
  • Lack of standardized technological tools hinders transparent and verifiable research practices.

Purpose of the Study:

  • To introduce modern open-source computational environments that promote reproducibility in psychology.
  • To guide emerging psychology researchers in adopting these technologies throughout the research lifecycle.

Main Methods:

  • Presentation of data archiving platforms for public dataset availability.
  • Advocacy for R as the standard for reproducible statistical analysis in psychology.
  • Description of dynamic report generation for integrating text, analysis, and outputs in manuscripts.

Main Results:

  • Demonstration of technologies that support data archiving, reproducible analysis, and integrated manuscript creation.
  • Provision of supplementary materials to facilitate the adoption of these tools.

Conclusions:

  • Open-source computational environments are essential for fostering reproducibility in psychological research.
  • Adoption of these technologies by emerging researchers is crucial for scientific integrity and transparency.