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Uncertainty: Overview00:59

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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.
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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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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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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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Counting is the type of measurement that is free from uncertainty, provided the number of objects being counted does not change during the process. Such measurements result in exact numbers. By counting the eggs in a carton, for instance, one can determine exactly how many eggs are there in the carton. Similarly, the numbers of defined quantities are also exact. For example, 1 foot is exactly 12 inches, 1 inch is exactly 2.54 centimeters, and 1 gram is exactly 0.001 kilograms. Quantities...
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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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Georgia Panagiotidou, Houda Lamqaddam, Jeroen Poblome

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    Humanistic data visualizations must communicate uncertainty. This study reviews digital humanities research, identifying how uncertainty is handled and proposing new visualization strategies for more trustworthy digital humanities research.

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    Area of Science:

    • Digital Humanities
    • Information Visualization
    • Humanities Research

    Background:

    • Humanistic data inherently contains uncertainty from historical sources.
    • Visualizations in digital humanities must accurately represent this uncertainty to maintain researcher trust.
    • Existing visualization methods may oversimplify complex humanistic data, obscuring crucial uncertainty.

    Purpose of the Study:

    • To systematically review how uncertainty is handled and represented in digital humanities visualizations.
    • To understand the relationship between uncertainty and visualization types in humanistic research.
    • To develop a foundation for more trustworthy, uncertainty-aware visualizations in the digital humanities.

    Main Methods:

    • Systematic literature review of 126 digital humanities publications.
    • Analysis of uncertainty sources, coping strategies, and visualization encoding.
    • Categorization of uncertainty representation approaches in visualizations.

    Main Results:

    • Uncertainty in humanistic data arises from multiple stages, from source artifacts to datafication.
    • Humanists employ strategies beyond data exclusion, valuing uncertainty as a dimension to retain.
    • Four distinct approaches to encoding uncertainty in visualizations were identified, varying in explicitness and customization.

    Conclusions:

    • This research provides empirical taxonomies of uncertainty and coping strategies for digital humanities.
    • It establishes a research agenda for uncertainty visualization in the digital humanities.
    • Findings promote synergy between humanists and visualization researchers, leading to more trustworthy visualizations.