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Social and Behavioral Sciences Commons™
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Full-Text Articles in Social and Behavioral Sciences
Sound Judgment: Review Of Noise: A Flaw In Human Judgment (2021) By Daniel Kahneman, Olivier Sibony, And Cass R. Sunstein, Anne Kelly
Numeracy
In Noise: A Flaw in Human Judgment (2021), Daniel Kahneman, Olivier Sibony, and Cass R. Sunstein convincingly demonstrate the pervasiveness and harmfulness of unwanted internal variability or noise. Using examples from both public and private sectors to demonstrate the quality and limits of the judgments we make, they argue that, despite objections based on possible cost, difficulty, and dehumanization, the reduction of noise is imperative for the fairness and equitability of systems upon which we depend.
How Random Noise And A Graphical Convention Subverted Behavioral Scientists' Explanations Of Self-Assessment Data: Numeracy Underlies Better Alternatives, Edward Nuhfer, Steven Fleisher, Christopher Cogan, Karl Wirth, Eric Gaze
How Random Noise And A Graphical Convention Subverted Behavioral Scientists' Explanations Of Self-Assessment Data: Numeracy Underlies Better Alternatives, Edward Nuhfer, Steven Fleisher, Christopher Cogan, Karl Wirth, Eric Gaze
Numeracy
Despite nearly two decades of research, researchers have not resolved whether people generally perceive their skills accurately or inaccurately. In this paper, we trace this lack of resolution to numeracy, specifically to the frequently overlooked complications that arise from the noisy data produced by the paired measures that researchers employ to determine self-assessment accuracy. To illustrate the complications and ways to resolve them, we employ a large dataset (N = 1154) obtained from paired measures of documented reliability to study self-assessed proficiency in science literacy. We collected demographic information that allowed both criterion-referenced and normative-based analyses of self-assessment data. …
Random Number Simulations Reveal How Random Noise Affects The Measurements And Graphical Portrayals Of Self-Assessed Competency, Edward Nuhfer, Christopher Cogan, Steven Fleisher, Eric Gaze, Karl Wirth
Random Number Simulations Reveal How Random Noise Affects The Measurements And Graphical Portrayals Of Self-Assessed Competency, Edward Nuhfer, Christopher Cogan, Steven Fleisher, Eric Gaze, Karl Wirth
Numeracy
Self-assessment measures of competency are blends of an authentic self-assessment signal that researchers seek to measure and random disorder or "noise" that accompanies that signal. In this study, we use random number simulations to explore how random noise affects critical aspects of self-assessment investigations: reliability, correlation, critical sample size, and the graphical representations of self-assessment data. We show that graphical conventions common in the self-assessment literature introduce artifacts that invite misinterpretation. Troublesome conventions include: (y minus x) vs. (x) scatterplots; (y minus x) vs. (x) column graphs aggregated as quantiles; line …