ArticleStatistical analysis and data mining2016
Use and Communication of Probabilistic Forecasts.
Article in Statistical analysis and data mining, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
14 citing papers in PubMed.
- A Software Platform for Collaborative Infectious Disease Modeling.Nature health · 2026Article
- Coordinating collaborative infectious disease modeling projects with the hubverse.medRxiv : the preprint server for health sciences · 2025Article
- Projecting Uncertainty in Ecosystem Persistence Under Climate Change.Global change biology · 2025Article
- Scenario design for infectious disease projections: Integrating concepts from decision analysis and experimental design.Epidemics · 2024Article
- Setting Health Targets Using Information from Probabilistic Projections: A Research Brief on an Application to Contraceptive Coverage.Population research and policy review · 2023Article
- Predicting corn tiller development in restrictive environments can be achieved to enhance defensive management decision tools for producers.Frontiers in plant science · 2023Article
- The United States COVID-19 Forecast Hub dataset.Scientific data · 2022Article
- Near-term forecasting of companion animal tick paralysis incidence: An iterative ensemble model.PLoS computational biology · 2022Article
- Bayesian Model Averaging to Account for Model Uncertainty in Estimates of a Vaccine's Effectiveness.Clinical epidemiology · 2022Article
- Probabilistic Projection of Subnational Life Expectancy.Journal of official statistics · 2021Article
- Advancing estuarine ecological forecasts: seasonal hypoxia in Chesapeake Bay.Ecological applications : a publication of the Ecological Society of America · 2021Article
- Communicating uncertainty about facts, numbers and science.Royal Society open science · 2019Review
- Letter to the Editor: Probabilistic population forecasts for informed decision making.Journal of official statistics · 2015Article
- Probabilistic population forecasting: Short to very long-term.International journal of forecastingArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
1 author.
Funding
Abstract
Probabilistic forecasts are becoming more and more available. How should they be used and communicated? What are the obstacles to their use in practice? I review experience with five problems where probabilistic forecasting played an important role. This leads me to identify five types of potential users: Low Stakes Users, who don't need probabilistic forecasts; General Assessors, who need an overall idea of the uncertainty in the forecast; Change Assessors, who need to know if a change is out of line with expectatations; Risk Avoiders, who wish to limit the risk of an adverse outcome; and Decision Theorists, who quantify their loss function and perform the decision-theoretic calculations. This suggests that it is important to interact with users and to consider their goals. The cognitive research tells us that calibration is important for trust in probability forecasts, and that it is important to match the verbal expression with the task. The cognitive load should be minimized, reducing the probabilistic forecast to a single percentile if appropriate. Probabilities of adverse events and percentiles of the predictive distribution of quantities of interest seem often to be the best way to summarize probabilistic forecasts. Formal decision theory has an important role, but in a limited range of applications.
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.