Research in fields such as weather forecasting has shown that people tend to be accepting of aleatory uncertainty when projecting into the future [1–3], but there is no consensus on which formats best help communicate the uncertainties appropriately.
Good communication of uncertainty gives the audience a sense of what experts think the chances are of a range of different outcomes, along with a sense of how confident they are in those forecasts. The audience should not be surprised by, or have their trust undermined by, outcomes manifesting that were not considered the most likely, but did not fall outside the bounds of expectation presented to them. When experts are less confident in their forecasts (for example, though less consensus), the audience’s expectations of the range of possible outcomes should broaden accordingly.
An empirical study, on the communication of uncertainty around Covid-19 vaccine effectiveness using a text format, suggested that the communication of a range of possible effectiveness (“between 50% and 70% effective”) – alongside an explanation for the uncertainty (“The reason I can’t give a more precise estimate is because the data we have doesn’t allow that”) – led to a lower decrease in trust and confidence in the communicator when participants were then told that the vaccine’s effectiveness was “actually nearer 40%” than when participants had been given an effectiveness estimate of 60%, with no uncertainty communicated [4]. This potential effect on future trust is also an important outcome for communicators and experts.
There have been a number of empirical studies into the effects of forecast communication, particularly in hurricane forecasting. The geospatial path of hurricanes is usually forecast using an ensemble of different models, but then combined into a summary display to be easier to understand [5], however summary displays in general can be misinterpreted both by the public and by experts [6–8]. Detailed work on the effects of hurricane track forecast communication has shown issues with both summary plots such as the ‘cone of uncertainty’ and with showing individual tracks within an ensemble [5,9].
An evaluation of fan charts for the forecast of population statistics suggest that they are broadly well-understood [10], but they have not been compared with ensemble plots.
The range of formats created at the end of October 2020 for the communication of uncertain forecasts provides an excellent opportunity to evaluate their effects, using outcome measures that evaluate the communication of the likelihoods of different outcomes (via the degree of surprise if they manifest), and perceived trustworthiness of the communicator (both before and after the actual outcome is known).