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Related Concept Videos

Uncertainty in Measurement: Accuracy and Precision03:37

Uncertainty in Measurement: Accuracy and Precision

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.
Statistical Analysis: Overview01:11

Statistical Analysis: Overview

When we take repeated measurements on the same or replicated samples, we will observe inconsistencies in the magnitude. These inconsistencies are called errors. To categorize and characterize these results and their errors, the researcher can use statistical analysis to determine the quality of the measurements and/or suitability of the methods.
One of the most commonly used statistical quantifiers is the mean, which is the ratio between the sum of the numerical values of all results and the...
Random and Systematic Errors01:20

Random and Systematic Errors

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...
Random and Systematic Errors01:20

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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...
Uncertainty: Overview00:59

Uncertainty: Overview

In analytical chemistry, we often perform repetitive measurements to detect and minimize inaccuracies caused by both determinate and indeterminate errors. Despite the cares we take, the presence of random errors means that repeated measurements almost never have exactly the same magnitude. The collective difference between these measurements - observed values - and the estimated or expected value is called uncertainty. Uncertainty is conventionally written after the estimated or expected value.
The Uncertainty Principle04:08

The Uncertainty Principle

Werner Heisenberg considered the limits of how accurately one can measure properties of an electron or other microscopic particles. He determined that there is a fundamental limit to how accurately one can measure both a particle’s position and its momentum simultaneously. The more accurate the measurement of the momentum of a particle is known, the less accurate the position at that time is known and vice versa. This is what is now called the Heisenberg uncertainty principle. He mathematically...

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Detection of Rare Genomic Variants from Pooled Sequencing Using SPLINTER
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Inaccuracy, uncertainty and the space-time permutation scan statistic.

Nicholas Malizia1

  • 1GeoDa Center for Geospatial Analysis and Computation, School of Geographical Sciences and Urban Planning, Arizona State University, Tempe, Arizona, USA. nmalizia@asu.edu

Plos One
|February 15, 2013
PubMed
Summary

The space-time permutation scan statistic (STPSS) is robust to inaccurate or incomplete data. This method for detecting spatio-temporal clusters shows minimal impact from data deficiencies.

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Area of Science:

  • Epidemiology
  • Geographic Information Systems (GIS)
  • Spatial Statistics

Background:

  • The space-time permutation scan statistic (STPSS) is widely used for identifying spatio-temporal clusters.
  • Little research has addressed the impact of inaccurate or incomplete data on STPSS results.
  • Spatio-temporal datasets frequently contain inaccuracies and uncertainties.

Purpose of the Study:

  • To investigate the effect of spatial/temporal accuracy and completeness deficiencies in input data on STPSS results.
  • To assess the robustness of STPSS when faced with imperfect spatio-temporal data.

Main Methods:

  • Simulation experiments using both synthetic and real-world spatio-temporal data.
  • Systematic introduction of inaccuracies and incompleteness into spatial and temporal data.
  • Evaluation of STPSS performance under various data quality degradation levels.

Main Results:

  • The STPSS demonstrated surprising robustness to deficiencies in spatial and temporal accuracy and data completeness.
  • Increased data degradation led to greater variability in STPSS results, as expected.
  • Weaker spatio-temporal interaction signals were more affected by data deficiencies than stronger signals.

Conclusions:

  • The STPSS is relatively resilient to common data quality issues found in spatio-temporal datasets.
  • Unlike global tests, this local spatio-temporal analysis metric is minimally impacted by data inaccuracies and incompleteness.