| People often have difficulty correctly interpreting Probability of Precipitation (PoP) forecasts - for example, people often interpret a 30% chance of rain to mean that it will rain 30% of the time. Specifying exactly what forecasts mean in a fluent, accessible, and easily understood way could help to correct this (Abraham et al. 2015; Gigerenzer et al. 2005; Juanchich & Sirota 2018). |
00301, 09301, 14701, 14702, 14703 |
3 |
1.70 |
3.00 |
2.57 |
| Forecasts are not interpreted in isolation; non-experts interpret recent forecasts in light of what previous forecasts have said. A “moderate” risk will cause more worry if it has been upgraded from a “low” risk than if it has been downgraded from a “high” risk, for instance (Sigrid, Moyner, Hohle, & Teigen 2015; Løhre 2018 [Studies 2-5]; Windschitl & Weber 1999 [Study 4]). |
27401, 18302, 18303, 18304, 18305, 31604 |
3 |
1.92 |
2.17 |
2.36 |
| The vast majority of people are intuitively aware that even deterministic forecasts are inherently uncertain (Savelli & Joslyn 2012; Joslyn & Savelli 2010; Morss et al. 2008; 2010). |
26401, 14101, 20701, 20801 |
2 |
2.06 |
3.00 |
2.35 |
| Motivated reasoning and general worldview can have a substantial impact on how people interpret uncertainty information, particularly in how they interpret the likelihood of various points within a confidence interval (Dieckmann et al 2017). |
05301, 05302 |
2 |
3.00 |
2.00 |
2.33 |
| The size of a confidence interval can influence how people interpret a forecast - people understand smaller confidence intervals to mean that a forecast is more technologically advanced and more certain (Teigen, Løhre, & Hohle 2018). |
29901, 29902, 29903, 29904, 29905 |
3 |
2.35 |
1.60 |
2.32 |
| Individuals tend to anchor to the severity of warnings regardless of order issued (Losee et al. 2017 [Study 1 & 2]). |
18801, 18802 |
2 |
1.62 |
3.00 |
2.21 |
| Including probabilistic / uncertainty information increases trust in expert forecasts. (Nakayachi, Johnson, & Koketsu 2018; Howe et al 2019). |
21501, 12701 |
2 |
2.50 |
1.75 |
2.08 |
| Both experts and non-experts’ interpretations of forecasts tend to be skewed towards the end of the forecast period, even in short-term forecasts (i.e. if there were an X% chance that a given event would occur sometime in a given week, people will, on average, perceive that the event is more likely to happen on Friday than on Monday) (Doyle et al. 2014; McClure, H. Doyle, & Velluppillai 2015; Morss et al. 2016). |
06101, 19601, 20901 |
2 |
1.83 |
2.33 |
2.06 |
| People do not always recognize uncertainty, and often express a preference for visualizations to aid understanding (Johnson & Slovic 1995; 1998). |
13401, 13501 |
2 |
1.88 |
2.25 |
2.04 |
| People intuitively seek out usable comparisons when interpreting probabilistic information (e.g. when asked to estimate the prevalence of a particular disease among women, people intuitively base their judgements on the given prevalence of the disease in men, even if the two are explicitly said to be unrelated). These comparisons combine with what we typically think of as objective probabilistic reasoning to shape feelings and attitudes about risks (P.D. Windschitl, Martin, & Flugstad 2002). |
22401, 22402, 22403, 22404 |
2 |
2.12 |
1.75 |
1.96 |
| Though confidence intervals are usually meant to denote that probabilities are normally distributed around a mean, most likely value, a plurality of people understand confidence intervals to mean that all values within the range were equally likely, an interpretation which is often incorrect. Clarifying how confidence intervals should be interpreted could help to offset this misunderstanding (Dieckmann et al 2015). |
05201, 05202, 05203 |
2 |
1.83 |
2.00 |
1.94 |
| Providing context-rich and easily understood risk comparisons (e.g. “X risky activity is about as risky as smoking a pack of cigarettes every two days”) can help facilitate understanding, particularly with small or long-term risks (Kunreuther, Novemsky, & Kahneman 2001). |
17101 |
1 |
2.75 |
2.00 |
1.92 |
| Individuals who believe that science is a debate rather than a search for truth tend to be more willing to act on uncertain information (Rabinovich & Morton 2012 [Study 1 & 2]). |
24601, 24602 |
2 |
1.25 |
2.50 |
1.92 |
| The increase in trust associated with including uncertainty information may be offset / eliminated by the inclusion of a statement acknowledging the inherent epistemological uncertainty of scientific forecasts. In general, giving people usable information about forecast uncertainty seems to increase trust, but general or open-ended acknowledgements of uncertainty can undermine trust (Howe et al. 2019). |
12701 |
1 |
2.75 |
2.00 |
1.92 |
| Less numerate people tend to focus on narrative evidence when evaluating risk communications (the context, their perceptions regarding the likelihood of comparable events, etc.), while more numerate people tend to focus on the stated probability of the risk. It is important to consider both elements in order to effectively reach everyone (Dieckmann, Slovic, & Peters 2009). |
05601, 05602 |
2 |
1.62 |
2.00 |
1.88 |
| Peoples’ understanding of uncertainty information often depends on what aspects of the forecast are emphasized by a forecaster (Wilson et al. 2019). |
31501 |
1 |
1.50 |
3.00 |
1.83 |
| Improve accuracy of multiple, overlapping risk estimates with probabilistic information used in combination with mechanism information (Dawson, Johnson, & Luke 2013). |
04701 |
1 |
2.25 |
2.00 |
1.75 |
| Often, probabilities themselves are easily understood but the events to which they refer are unclear to members of the public. Being as specific as possible about what events are being described should help facilitate better understanding, particularly for Probability of Precipitation (PoP) forecasts (Murphy et al. 1980). |
21201 |
1 |
1.00 |
3.00 |
1.67 |
| Forecasts that are more uncertain tend to elicit more risk-averse behavior (Ramos, van Andel, & Pappenberger 2013). |
24801 |
1 |
1.50 |
2.50 |
1.67 |
| When presented with a choice between two risky options, one of which has a higher absolute number of “good” outcomes (e.g. a 3/50 chance) and one of which has a higher proportion of “good” outcomes (e.g. a 1/10 chance), people will often neglect the denominator and choose the latter, even when they admit to knowing that the odds were better with the former (Denes-Raj & Epstein 1994). |
05001, 05002 |
2 |
2.00 |
1.00 |
1.67 |
| More numerate people tend to have more accurate understandings of risk information, regardless of format (Gardner et al. 2011). |
09001 |
1 |
2.00 |
2.00 |
1.67 |
| Provide a clear definition of PoP in light of confusion over its meaning among both experts and the general public (Stewart et al. 2015). |
28201 |
1 |
1.00 |
2.50 |
1.50 |
| Using a scale format can help to reduce additivity neglect (i.e. the tendency to provide responses totaling to over 100% when asked to estimate the probabilities of an exhaustive list of multiple events) (Riege & Teigen 2013). |
25401 |
1 |
1.75 |
1.00 |
1.25 |
| Upward revisions to forecasts generally lead individuals to perceive events as closer than downward changes (Maglio & Polman 2016 [Study 10]). |
18901 |
1 |
1.00 |
1.00 |
1.00 |