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Related Concept Videos

Interpretation of Confidence Intervals01:19

Interpretation of Confidence Intervals

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A confidence interval is a better estimate of the population than a point estimate, as it uses a range of values from a sample instead of a single value.
Confidence intervals have confidence coefficients that are crucial for their interpretation. The most common confidence coefficients are 0.90, 0.95, and 0.99, which can be written as percentages–90%, 95%, and 99%, respectively.
Suppose a person calculates a confidence interval with a confidence coefficient of 0.95. In that case, they can...
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Uncertainty: Confidence Intervals00:54

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The confidence interval is the range of values around the mean that contains the true mean. It is expressed as a probability percentage. The interpretation of a 95% confidence interval, for instance, is that the statistician is 95% confident that the true mean falls within the interval. The upper and lower limits of this range are known as confidence limits. The confidence limits for the true mean are estimated from the sample's mean, the standard deviation, and the statistical factor...
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Propagation of Uncertainty from Systematic Error01:10

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The atomic mass of an element varies due to the relative ratio of its isotopes. A sample's relative proportion of oxygen isotopes influences its average atomic mass. For instance, if we were to measure the atomic mass of oxygen from a sample, the mass would be a weighted average of the isotopic masses of oxygen in that sample. Since a single sample is not likely to perfectly reflect the true atomic mass of oxygen for all the molecules of oxygen on Earth, the mass we obtain from this...
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Errors and Mistakes in Surveying01:19

Errors and Mistakes in Surveying

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Errors and mistakes in surveying refer to inaccuracies in measurements and data recording. The errors are deviations from the actual value caused by human sensory limitations, equipment flaws, or environmental effects. These errors are typically unintentional and can result from the inherent imperfections in the instruments used, atmospheric conditions, or the observer’s inability to perceive exact measurements. On the other hand, mistakes are caused by the surveyor's lack of...
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Hindsight Biases01:12

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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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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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Data Processing Methods for 3D Seismic Imaging of Subsurface Volcanoes: Applications to the Tarim Flood Basalt
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Implicit Error, Uncertainty and Confidence in Visualization: An Archaeological Case Study.

Georgia Panagiotidou, Ralf Vandam, Jeroen Poblome

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    Visualizing qualitative uncertainty in archaeological data helps novices recognize overlooked issues and boosts experts

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    Area of Science:

    • Digital Humanities
    • Archaeology
    • Information Visualization

    Background:

    • Quantifiable uncertainty visualization impacts insight confidence.
    • Qualitative uncertainty, inherent to data, is less understood.
    • Assessing qualitative uncertainty is crucial for understanding spatio-temporal patterns in archaeology.

    Purpose of the Study:

    • Investigate the impact of visualizing qualitative implicit errors on the sense-making process.
    • Understand how different user expertise levels interact with such visualizations.

    Main Methods:

    • Developed a visualization probe representing three implicit errors: differing collection methods, subjective interpretations, and temporal continuity assumptions.
    • Analyzed interactions of 14 archaeologists with varying domain expertise.
    • Collected qualitative feedback on uncertainty and visualization effectiveness.

    Main Results:

    • Novice archaeologists became more aware of typically overlooked data issues.
    • Domain experts reported increased confidence in the visualization.
    • Participants discussed social factors, requested more data context, and gained meta-insights on methodological directions.

    Conclusions:

    • Visualizing qualitative uncertainty can enhance data awareness and confidence.
    • Findings inform future visualizations for handling implicit errors in data-critical domains like digital humanities.
    • Effective visualization design should consider user typologies and data complexity.