Communicating Probability Forecasts – will people understand?
Authors: Mylne Ken 
Published: December 2024

Executive Summary
“People don’t understand probabilities” – or do they? Weather forecasting science has long
been developing ensemble forecasts as a way to improve forecast capability and provide
better information to support users’ decisions. The science is well proven and, indeed, the
Met Office will soon move to an ensemble-only NWP (Numerical Weather Prediction)
system. Ensemble forecasts can be used in a number of ways, but fundamentally they
provide a probabilistic picture of the weather forecast including a most likely outcome and
information on the confidence, uncertainty or risks associated with forecast outcomes. In
order to pull through the full benefits of this information it is important to communicate this
information effectively to users so that they can make appropriate risk-based decisions.
There is a widely-held belief that people will find probabilistic information hard to understand
or make use of, which provides a significant obstacle to communicating it.
This challenge for ensemble or probabilistic forecasts has long been recognised and there
has been extensive research conducted into effective communication and people’s
understanding of such forecasts, including several papers led or sponsored by the Met
Office. This paper offers a review of that research to help guide future communications of
forecasts. The overwhelming and consistent conclusion found in the literature is that
people do understand the probabilistic information and make better decisions when
presented with it, provided that the information is presented appropriately.
Key conclusions include:
• Nearly all of the studies indicate that people make better decisions, have more trust
in information, and/or display more understanding of forecast information when
forecasters use probability information in place of deterministic statements.
• Providing additional information on uncertainty does not lead to confusion and
misinterpretation compared to simple deterministic forecasts.
• The inclusion of a numerical probability (e.g. 30%) alongside a visual or worded
description can greatly help with correct interpretation; using both forms helps ensure
that both more and less numerate individuals will understand the message.
• Careful choice of language helps to promote understanding e.g. some people may be
put off by “30% probability” which they consider to be mathematical, but are quite
comfortable with “30% chance” and interpret it correctly.
• It is important clearly define the events to which probabilities apply, and the way in
which forecasters frame messages can influence how audiences interpret risks.

Overall, the literature review provides strong support for communicating probabilistic
information to forecast users, including the general public. It does not support the idea that
people’s understanding should be a barrier to communicating such information. While not
every single person will understand or take full advantage of the additional information, most
people will benefit and make better decisions as a result. The review also offers a number of
suggestions for optimising effective communication.