Organizations tasked with communicating expert judgments couched in uncertainty often use numerically bounded linguistic probability schemes to standardize the meaning of verbal probabilities. An experiment (N = 1,202) was conducted to ascertain whether agreement with such a scheme was better when probabilities were presented verbally, numerically or in a combined “verbal + numeric” format. Across three agreement measures, the numeric and combined formats outperformed the verbal format and also yielded better discrimination between low and high probabilities and were less susceptible to the fifty-fifty blip phenomenon. The combined format did not confer any advantage over the purely numeric format. The findings indicate that numerically bounded linguistic probability schemes are an ineffective means of communicating information about probabilities to others and they call into question recommendations for use of the combined format for delivering such schemes.

Everyone faces uncertainty on a daily basis. Two kinds of probability expressions, verbal and numerical, have been used to characterize the uncertainty that we face. Because our cognitive concept of living things differs from that of non-living things, and distinguishing cognitive concepts might have linguistic markers, we designed four studies to test whether people use different probability expressions when faced with animate or inanimate uncertainty. We found that verbal probability is the preferred way to express animate uncertainty, whereas numerical probability is the preferred way to express inanimate uncertainty. The “verbal-animate” and “numerical-inanimate” associations were robust enough to persist when tested with forced-choice response patterns regardless of the information (e.g., equally likely outcomes, frequencies, or personal beliefs) used to construct probabilities of events. When the response pattern was changed to free-responses, the associations were evident unless the subjects were asked to write their own probability predictions for vague uncertainty. Given that the world around us consists of both animate (i.e., living) and inanimate (i.e., non-living) things, “verbal-animate” and “numerical-inanimate” associations may play a major role in risk communication and may otherwise be useful for practitioners and consultants.

Research on forecasting is effectively limited to forecasts that are expressed with clarity; which is to say that the forecasted event must be sufficiently well-defined so that it can be clearly resolved whether or not the event occurred and forecasts certainties are expressed as quantitative probabilities. When forecasts are expressed with clarity, then quantitative measures (scoring rules, calibration, discrimination, etc.) can be used to measure forecast accuracy, which in turn can be used to measure the comparative accuracy of different forecasting methods. Unfortunately most real world forecasts are not expressed clearly. This lack of clarity extends to both the description of the forecast event and to the use of vague language to express forecast certainty. It is thus difficult to assess the accuracy of most real world forecasts, and consequently the accuracy the methods used to generate real world forecasts. This paper addresses this deficiency by presenting an approach to measuring the accuracy of imprecise real world forecasts using the same quantitative metrics routinely used to measure the accuracy of well-defined forecasts. To demonstrate applicability, the inferred probability method is applied to measure the accuracy of forecasts in fourteen documents examining complex political domains.

Applied to uncertainty communication, politeness theory postulates that when announcing bad news (1) speakers may intend not only to inform, but also to manage (e.g., save) the hearers’ or speakers’ own faces (i.e., face-managing intentions), and (2) speakers may perform face-managing intentions by altering the explicitly communicated probability. Previous research has assumed these two core postulates when explaining various reasoning and judgment phenomena in hearers, but has failed to test them empirically in a comprehensive and direct way: jointly in relation to speakers. To provide this critical evidence, we asked subjects to communicate a predefined numerical probability of two negative outcomes, using a verbal probability scale. Subjects reported their communication intentions afterwards. In line with the first politeness theory postulate, speakers intended not only to be informative but also to tactfully announce bad news or to avoid being blamed in case they made inaccurate (too low or too high) prediction. In line with the second politeness theory postulate, speakers altered their explicitly communicated probability more often and more substantially when adopting face-managing intentions than when adopting informative intentions. We discuss how this evidence corroborates the politeness theory and validates the previous research that focused on hearers.

Recent research suggests that communicating probabilities numerically rather than verbally benefits forecasters’ credibility. In two experiments, we tested the reproducibility of this communication-format effect. The effect was replicated under comparable conditions (low-probability, inaccurate forecasts), but it was reversed for low-probability accurate forecasts and eliminated for high-probability forecasts. Experiment 2 further showed that verbal probabilities convey implicit recommendations more clearly than probability information, whereas numeric probabilities do the opposite. Descriptively, the findings indicate that the effect of probability words versus numbers on credibility depends on how these formats convey directionality differently, how directionality implies recommendations even when none are explicitly given, and how such recommendations correspond with outcomes. Prescriptively, we propose that experts distinguish forecasts from advice, using numeric probabilities for the former and well-reasoned arguments for the latter.