Similarities & Differences Between Inferential Statistics and Global Haiku: Follow-up
Abstract Number:
1449
Submission Type:
Contributed Abstract
Contributed Abstract Type:
Speed
Participants:
Charles Smith (1), David McMurray (2)
Institutions:
(1) North Carolina State Univ., N/A, (2) Kagoshima International University, Kagoshima, Kagoshima
Co-Author:
First Author:
Presenting Author:
Abstract Text:
In this poster, we provide additional background and examples for our Feb. 2025 paper in Chance. By juxtaposing statistical estimates which strive to be precise and accurate, with haiku that are more often purposely ambiguous or have several nuanced meanings, we raise the idea that the main similarity between statistics and haiku is that both are a reduction in dimensionality. Statistics takes many data points to a single or a set of numerical summaries or coefficients in a model, while haiku places "moments in time" into 3-line verse to convey gist which may be multisensory and filled with the poet's poignant feelings. To demonstrate this contiguity, we begin by introducing readers to pop-culture haiku and literary haiku with examples of both, then briefly refresh readers with the patterns of praxis in descriptive and inferential statistics. We provide examples of regression to illustrate dimension reduction and side by side boxplots in a biomechanical data. Additional similarities include: imagery (data visualization), hypothesis testing vs. 3rd-line of haiku, pairing and contrast. Next the current research use of AI in haiku is explored. Finally Stefanski-style residuals.
Keywords:
haiku|regression diagnostics|data visualization|AI haiku|basketball plots|
Sponsors:
Quantitative Communication Interest Group
Tracks:
Miscellaneous
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