Dealing with uncertainty

People in protective masks at the grocery store
Photo by Lucas Santos on Unsplash

What happened

During the lockdown, I have started to cook. I love that cooking at home is like solving a puzzle. It would help if you had a strategy on how to combine ingredients piled up in the fridge so that you can provide a variety of meals in days. When it comes to grocery shopping, I feel the expected anxiety of going to the store in person. I take some comfort in getting there in less busy hours, which can be googled. Around 8:00 PM, it is supposed to be less crowded in the store where I go. Usually, it works this way, and when I get there at 8:00 PM, there are one or two people at the checkout. But one day I went in at the usual time and was surprised to see four people. I felt intrigued and checked the online store’s info if there were changes in popular times. But it turned out that there were no changes. I just needed to read the schedule like this: on average, there are two people, and maybe two more or less. That is why I started to wonder if there are ways to show the data’s uncertainty visually.

Graph with popular times at the grocery store
Popular times at my store

Scientific uncertainty

The ambiguity in the display of data is widespread. For instance, a weather app forecasts snow, you put on rubber boots, but there is no snow. Or you wait for a bus, according to the schedule it should arrive, but the bus is late. The gap between weather forecasts or bus schedules and reality is incredibly frustrating if you understand bar charts or other graphs as an undeniable image of “what is.” In practice, you can rarely be 100% sure because data is constructed, not just given as a natural reflection of existing facts.

All avocados from the garden are compared to a random sample of avocados
Mean values and confidence intervals for avocados’ weight

What to do

The most straightforward approach to displaying a confidence interval is to use a point to show the sample mean and lines to show the range around the mean [1]. Although this kind of graph is standard in the statistics, it has shortcomings. Sometimes the error bars cannot reflect the reality of data well. One might want to know whether the data elements are distributed evenly or clustered. If clustered, how many clusters and where? Are there any unusual data elements? Besides, approaches developed in scientific publications require basic knowledge of statistics. Many people don’t know what a confidence interval is.

Three driver ratings are compared, which have a mean value and confidence intervals
Mean driver ratings and associated confidence intervals
History of ratings of all drivers in the park with confidence intervals
Rating history with confidence intervals

Conclusion

People use visualizations to make a variety of decisions, from everyday transit decisions to healthcare communications. Uncertainty is inherent in most data and can enter the collection, modeling, and analysis stage, although people usually do not assume this. Displaying uncertainty in graphs is a smart way to encourage your audience to consider uncertainty when making decisions. The difficulty is that there is still no established solution to showing it, although the topic is being discussed [4,5]. A clear understanding of what researchers mean by scientific uncertainty, and where it can be measured and where it can’t, would help everyone figure out how to respond to uncertainty in the data.

References

  1. A chapter about uncertainty in the book “Fundamentals of Data Visualization,” published by Claus O. Wilke
  2. An article “Decision making with visualizations: a cognitive framework across disciplines” by Lace M. Padilla
  3. A survey “How to Assess Visual Communication of Uncertainty? A Systematic Review of Geospatial Uncertainty Visualisation User Studies” by Christoph Kinkeldey, Alan M. MacEachren, and Jochen Schiewe
  4. A thesis for the Degree of Master of Fine Arts in Information Design and Visualization by Zheng Yan Yu
  5. A research lab MU Collective working on uncertainty visualizations

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Olya Solovieva

Olya Solovieva

Senior product designer at Yandex Go. I design tools for taxi fleet managers