See also: the classic box-and-whisker plot, salary percentiles by industry and my attempt at animating potential values. You don’t get this from means. So here are some visualization options for the uncertainties in your data, each with its pros, cons, and examples. When data appears all at once or in aggregate, it can be a challenge for many to interpret results and link it back to what the data actually represents. But standard errors, confidence intervals, and likelihoods often lose their visual space in data graphics, which leads to judgements based on simplified summaries expressed as means, medians, or extremes. The more uncertain an estimate is, the more difficult it is to see, becoming less visually prominent compared to more certain estimates. But that might be a problem with the forecasting more than the chart choice. You can add uncertainty to your writing by avoiding absolutes when you describe numbers. People can see that there is no set path, and instead they see a bunch of possible paths. Data is a representation of real life. In their , they show range with light gray bars behind a black dot to represent possible player impact over time. After all, you don’t have to visualize. If you’re less certain about an estimate, make it less visually prominent. Distributions Show the spread of possible values with a histogram or a variant of it. The range is especially useful when you compare multiple estimates, because you can see overlap between categories. You miss out on the interesting stuff. I haven’t seen this done much, but the wind prediction map by Moritz Stefaner comes to mind. By showing the variation in a sample, you or a reader can make a more educated judgement about whether a sample is trustworthy. You might see something a median would never show. Simulations Similar to showing multiple outcomes, seeing various results occur one-by-one to build up an overall picture provides intuition for the fuzziness of predictions. Treat estimates as such when you use them, and account for the uncertainty in the numbers.
Heisenberg's uncertainty principle - ….
Dealing with the Uncertainty of Bipolar Disorder. Sometimes variation is just noise, or the details might obscure the forest for the trees.. Let’s start with the traditional visualization approach, which at the least is to show a range or confidence interval. Multiple Outcomes When it comes to projections and forecasts, it is helpful to see various outcomes to see what might happen. Uncertainty is displayed more explicitly. FiveThirtyEight often does a good job at valuing uncertainty in their work. Too much weight might be placed on individual outcomes which obscures the overall picture. A point in the middle represents a mean or median, and a bar or line shows other possible values or coverage. Relationship uncertainty. Lines or bars represent a range of values, so you can see that a mean or median represents only part of an estimate. To show simulation uncertainty for the election, The Upshot displayed multiple delegate outcomes at the same time using various models. If there’s too much noise or there are too many possibilities, the chart might not provide anything of use. Lines represent wind predictions, and opacity represents the strength of the predictions. By showing simulations, you get a sense of build-up and a link with individual outcomes. There’s a ton of variance when people experience a “first” in their relationship lives, so instead of just average ages, I used distributions. Datingbuzz s.a. If you have a full distribution of values, you don’t get to see all of the details in the data. B dating service. You can achieve this effect a number of ways, such as with transparency, color scale, or blurriness. See also: How people spend their time visualized with parallel coordinates. And of course, how can one forget the jittering gauge.
Also, a lot of people don’t understand the concept of confidence intervals or what standard error bars are, so you need to explain clearly with annotation. The data that’s less up in the air gets more attention as a result. The Social Security Administration puts out life expectancy and probabilities of death at any given age. Maybe visualization isn’t what you’re looking for at all. It’s an abstraction, and it’s impossible to encapsulate in a spreadsheet, which leads to uncertainty in the numbers. See also: Hurricane tracking and the fan chart for time series data, and bootstrap density curves