Communicating Uncertainty in National Security Intelligence: Expert and Non-expert Interpretations of and Preferences for Verbal and Numeric Formats

Organizations in several domains including national security intelligence communicate judgments under uncertainty using verbal probabilities (e.g., likely) instead of numeric probabilities (e.g., 75% chance), despite research indicating that the former have variable meanings across individuals. In the intelligence domain, uncertainty is also communicated using terms such as low, moderate, or high to describe the analyst’s confidence level. However, little research has examined how intelligence professionals interpret these terms and whether they prefer them to numeric uncertainty quantifiers. In two experiments (N = 481 and 624, respectively), uncertainty communication preferences of expert (n = 41 intelligence analysts inExperiment 1) and non-expert intelligence consumers were elicited. We examined which format participants judged to be more informative and simpler to process. We further tested whether participants treated probability and confidence as independent constructs and whether participants provided coherent numeric probability translations of verbal probabilities. Results showed that whereas most non-experts favored the numeric format, experts were about equally split, and most participants in both samples regarded the numeric format as more informative.Experts and non-experts consistently conflated probability and confidence. For instance, confidence intervals inferred from verbal confidence terms had a greater effect on the location of the estimate than the width of the estimate, contrary to normative expectation. Approximately ¼ of experts and over ½ of non-experts provided incoherent numeric probability translations of best estimates and lower and upper bounds when elicitations were spaced by intervening tasks.

Communicating Health Risks to the Public

<p xss=removed><span lang="EN-US" xss=removed>Health risks, such as the probability of experiencing a side effect from a medication, are typically communicated numerically. However, presenting risks in strictly numeric formats is problematic considering that the public often experiences difficulty in comprehending strictly numeric probabilities. To help overcome this problem, Leonhardt and Keller (2018) tested the efficacy of using pictographs to visually present probabilistic information to health consumers. They found that the addition of pictographs alongside numeric probability information increased probability comprehension and lessened the perceived risk of a multiple risk health option. Here, we review relevant work on probability format and build on the general evaluability theory to posit why pictographs may result in lower risk perceptions of multiple risk options. We discuss current limitations in our understanding of how the public perceives multiple risk options, and we highlight opportunities for future research. For instance, we introduce Quick Response (QR) codes as a potential tool to help consumers view health risks in multiple formats on the Internet.</span><br></p>

Statistical reporting of metabolomics data: experience from a high-throughput NMR platform and epidemiological applications

Introduction Meta-analysis is the cornerstone of robust biomedical evidence. Objectives We investigated whether statistical reporting practices facilitate metabolomics meta-analyses. Methods A literature review of 44 studies that used a comparable platform. Results Non-numeric formats were used in 31 studies. In half of the studies, less than a third of all measures were reported. Unadjusted P-values were missing from 12 studies and exact P-values from 9 studies. Conclusion Reporting practices can be improved. We recommend (i) publishing all results as numbers, (ii) reporting effect sizes of all measured metabolites and (iii) always reporting unadjusted exact P-values.

Using the File Command to Produce Formatted Output for other Applications

The file command provides a way to produce tables for use in other application software. It can be especially useful for combining descriptive results (such as means and percentages) and results from significance tests. Extracting and manipulating the results directly from Stata matrices gives more control over arrangement, while other Stata functions may be used to control numeric formats. This tutorial includes examples based on survey data of both plain text and HTML output.