The Sorcerer’s App
On January 28th, Nature published a brief news story by science writer and editor Sara Phillips about the latest threat from AI to online veracity, in this case, the virtual undetectability of chatbots responding to social science surveys. The centerpiece of the story was a peer-reviewed paper in the Proceedings of the National Academy of…
Narrative Landscapes, Part I:
The emperor has no clothes that fit
The term “landscape” has something powerfully seductive about it. The imagery it evokes is so appealing, that further thought can be completely suspended. — Jones (1995) Seductive indeed: use story-related data on a contour plot to identify clusters or loci of desirable and undesirable responses; look at plausible pathways across the “topography” between these loci;…
Statistics in the Triad, Part IX: Entropy, or How Much the Data Are Concentrated
The two previous posts in this series looked at where sensemaking story data are concentrated in a ternary. Part VIIIa explored the use of a non-parametric method for calculating smooth (continuous) contour lines, essentially a data-driven “guess” at the density of story points. Part VIIIb gave examples of a simpler, albeit less elegant, alternative —…
What rhymes with REDACTION?
At times, it is important to step in and protect the identity of someone sharing an experience in an Active Sensemaking collection to help keep anonymity. We ask people not to share identifiable details about themselves or the people they write about. Some projects center around sensitive or volatile subjects where identifying people or places…
Fiddling with data
I am always looking for new data visualizations that help stakeholders see something they might not understand with numbers or words. We can take the results from a Slider question and present it in its histogram format and most people will be able to interpret the responses overall. We can tell that the vast majority…
Statistics in the Triad, Part VIIIb: Binning, or Where the Data Are actually Concentrated
The first section, ‘Data distribution in a triad’, of Part VIIIa listed some of the quantitative methods for comparison of story data in triads, including confidence regions, point-counting of clusters, and smooth contouring. The last of these uses kernel density estimation (KDE) to calculate a probability density function (PDF) for the data. This statistical alphabet…
Statistics in the Triad, Part VIIIa: Smoothing, or Where the Data Are not Concentrated
This post is a cautionary tale — a warning buoy, if you will — about a widely-used method to aggregate and smooth data. The caution applies only to SenseMaker projects, which use ternary plots (triads) for both data collection and display, in particular those projects which yield high concentrations of data points near vertices. For…
Statistics in the Triad, Part VII: Mapping The Datasaurus Dozen
Note: If you landed here by searching on “datasaurus” (± “dozen”) and have no idea what SenseMaker is, you can jump to the graphical results. In an earlier post in this series, Part III: Random Data, I showed an example of a SenseMaker triad, with data clustered near vertices, along edges, and in the center;…
Statistics in the Triad, Part VI: The Story as Unit of Observation
If you had asked me a year ago to identify the primary unit of observation in a SenseMaker project, I would have said, without much hesitation, it’s the story, of course. When I started writing Part IV in this series on Confidence Regions, however, I had to revisit that question. I knew what was typically…
Statistics in the Triad, Part V: Closure and Causal Structure
Here’s one of those articles that I carry around, bound to me by neural Velcro, stored in Instapaper, and gestating in background mode: When Correlation Is Not Causation, But Something Much More Screwy. It’s a 2012 guest piece in The Atlantic by UCLA sociology professor Gabriel Rossman, merging two 2010 posts from his blog, Code…
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