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Related Concept Videos

Uncertainty: Overview00:59

Uncertainty: Overview

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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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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 Random Error00:59

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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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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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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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Approximate Integration01:24

Approximate Integration

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In many practical and theoretical contexts, the exact value of a definite integral may be inaccessible. This limitation typically arises when the antiderivative of a function is either unknown or cannot be expressed in a closed mathematical form. Alternatively, it can occur when a function is defined not by a formula but by a finite set of empirical data points, such as those collected during experiments. In these cases, approximate integration techniques provide a valuable solution.One of the...
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Related Experiment Video

Updated: Apr 30, 2026

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline
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Exploratory visualization involving incremental, approximate database queries and uncertainty.

Danyel Fisher, Steven M Drucker, A Christian König

    IEEE Computer Graphics and Applications
    |May 9, 2014
    PubMed
    Summary
    This summary is machine-generated.

    Incremental visualization systems offer faster query results from large datasets but reduce accuracy. Analyst feedback suggests alternative visualizations for ongoing queries, balancing speed and precision in data analysis.

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    Area of Science:

    • Data Visualization
    • Human-Computer Interaction
    • Information Retrieval

    Background:

    • Large datasets often lead to slow query performance, impacting user experience.
    • Incremental visualization systems provide preliminary results rapidly, sacrificing immediate accuracy.
    • Balancing query speed and data precision is a critical challenge in data analytics.

    Purpose of the Study:

    • To evaluate user feedback on incremental visualization systems for large datasets.
    • To identify limitations and areas for improvement in current incremental visualization techniques.
    • To explore alternative visualization methods for representing in-progress queries.

    Main Methods:

    • Analysts were tasked with using an incremental visualization system.
    • Qualitative feedback was collected from analysts regarding their experience.
    • Suggestions for alternative visualizations were solicited and analyzed.

    Main Results:

    • Analysts reported trade-offs between speed and accuracy with incremental visualizations.
    • User feedback highlighted the need for clearer representations of ongoing computations.
    • Specific alternative visualization concepts were proposed by the analysts.

    Conclusions:

    • Incremental visualization systems present a viable approach for accelerating data exploration.
    • Further research is needed to develop more accurate and informative visualizations for in-progress queries.
    • Analyst-driven insights are crucial for advancing the design of effective data visualization tools.