Related Experiment Video
Updated: Aug 27, 2025
05:37
An R-Based Landscape Validation of a Competing Risk Model
Published on: September 16, 2022
2.2K
Evaluating the Use of Uncertainty Visualisations for Imputations of Data Missing At Random in Scatterplots
IEEE Transactions on Visualization and Computer Graphics
|September 27, 2022
Summary
Visualizing imputed data in exploratory data analysis (EDA) can reduce bias but may decrease precision. Different uncertainty representations impact user accuracy and confidence when analyzing datasets with missing values.
Area of Science:
- Data Visualization
- Human-Computer Interaction
- Statistical Analysis
Background:
- Real-world datasets frequently contain missing values, yet many exploratory data analysis (EDA) systems primarily visualize complete cases.
- This limitation can lead to biased analyses and flawed insights.
- Imputation techniques address missing data but introduce uncertainty, complicating visualization.
Purpose of the Study:
- To investigate the impact of visualizing imputed data and imputation uncertainty on user bias and precision.
- To compare various uncertainty representations in scatterplots for tasks involving estimating averages and detecting trends.
- To assess user confidence in their analytical responses across different visualization methods.
Main Methods:
- A crowdsourced study involving 202 participants was conducted.
- Participants performed tasks of estimating averages and detecting trends using scatterplots with different imputation uncertainty visualizations (no imputation, mean, 95% confidence intervals, probability density plots, gradient intervals, hypothetical outcome plots).
- User bias, precision, and self-reported confidence were measured.
Main Results:
- For average estimation, uncertainty representations reduced bias but decreased precision compared to no imputation.
- For trend detection, only hypothetical outcome plots showed a potential to slightly reduce bias while increasing precision.
- Participants reported lower confidence across all uncertainty visualization conditions compared to the baseline (no imputation).
Conclusions:
- Visualizing imputation uncertainty presents trade-offs between bias reduction and precision maintenance.
- Hypothetical outcome plots may offer a nuanced approach for trend detection tasks.
- The choice of uncertainty visualization significantly influences user perception and analytical performance, especially with substantial missing data.
Related Concept Videos
00:54Uncertainty: Confidence Intervals
4.4K
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...
4.4K
00:59Uncertainty: Overview
811
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.
811
01:19Interpretation of Confidence Intervals
6.5K
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...
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...
6.5K
01:04Random Error
1.4K
Random or indeterminate errors originate from various uncontrollable variables, such as variations in environmental conditions, instrument imperfections, or the inherent variability of the phenomena being measured. Usually, these errors cannot be predicted, estimated, or characterized because their direction and magnitude often vary in magnitude and direction even during consecutive measurements. As a result, they are difficult to eliminate. However, the aggregate effect of these errors can be...
1.4K
00:57Modified Boxplots
9.9K
A standard box and whisker plot informs us about the spread of the data in a given sample. One can identify the minimum value, maximum value, first quartile value, second quartile or median value, and third quartile.
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
However, the box plot does not tell the reader about outliers - values that lie far from the center of the data. We can modify the standard box and whisker plot to identify the outliers and visualize the actual spread of the data in a sample.
Initially, we calculate the adjusted...
9.9K
01:15Scatter Plot
7.3K
The most common and easiest way to display the relationship between two variables, x and y, is a scatter plot. A scatter plot shows the direction of a relationship between the variables. A clear direction happens when there is either:
7.3K