Group items tagged

How should statisticians, journalists and designers reveal uncertainty in graphics for public consumption? Great short (12 page) essay with loads of examples and figures illustrating how we deal with uncertainty, and some of the problems people face when interpreting uncertainty - for example, when you see a map showing the path a hurricane is expected to take, how do you interpret the line? What does it mean?

Should newspapers be adding confidence intervals to their graphics? Why, she asked, are newspapers like hers hesitant to print confidence intervals, a statistical measure of uncertainty? With the exception of noting sampling error in polling data, newspapers like the Times only show uncertainty when they're forced to - and often to prove the opposite of what point data might show.

Data aren't dead, contrary to what some pundits stated post-election [2], rather the limitations of data are not always well reported. While pollsters will be reworking their models following the election, what can media journalists do to improve their overall coverage of statistical issues in the future? First, discuss possible statistical biases, such as errors in sampling and polling, and what impact these might have on the results. Second, always provide measures of uncertainty, and root these uncertainties in real-world examples.

These are uncertain times. The US presidential campaign has been divisive, there is disagreement over Brexit, jitters over China's economy and technology is disrupting traditional labour markets. What is the best way to weather all that uncertainty?

To communicate uncertainty in climate change models and predictions, the IPCC uses a range of expressions to describe the probability that a particular event will occur. For example, in the phrase: "It is very likely that heat extremes will become more frequent in the future," the phrase 'very likely' is used to describe a likelihood of more than 90 per cent, says Smithson.

Life is one long string of decisionmaking, even if none of them is major. This is certainly the impression we get from reading cognitive neuroscience journals. A great many studies these days seem to involve 'decisionmaking under uncertainty' (otherwise known as gambling). As a married couple, we have now clocked up just over 50 years of decisionmaking together. We still frequently avoid or delay decisions, but we know that this does not pay off in the long run. And, when we do make decisions, we usually make them jointly. In case this sounds too good to be true, we hasten to add that it's not always easy and often involves arguments - despite, or perhaps because, we are both cognitive neuroscientists ourselves. Actually, argument turns out to be a well-kept secret in group decisionmaking. But before we turn to the value of acrimony, let's look at some of the reasons why we believe that people can make better decisions together than they can on their own.

People use imprecise words to describe the chance of events all the time - "It's likely to rain," or "There's a real possibility they'll launch before us," or "It's doubtful the nurses will strike." Not only are such probabilistic terms subjective, but they also can have widely different interpretations. One person's "pretty likely" is another's "far from certain." Our research shows just how broad these gaps in understanding can be and the types of problems that can flow from these differences in interpretation.

Medicine's decades-long march toward patient autonomy means patients are often now asked to make the hard decisions - to weigh trade-offs, to grapple with how their values suggest one path over another. This is particularly true when medical science doesn't offer a clear answer: Doctors encourage patients to decide where evidence is weak, while making strong recommendations when evidence is robust. But should we be doing the opposite?People in general are not great at evaluating risk. They worry more about shark attacks than car crashes.

The Media Has A Probability Problem The media's demand for certainty - and its lack of statistical rigor - is a bad match for our complex world.