To thoroughly, accurately, and clearly inform, we must identify the intended signal and then boost it while eliminating as much noise as possible. This certainly applies to data visualization, which unfortunately lends itself to a great deal of noise if we’re not careful and skilled. The signal in a stream of content is the intended message, the information we want people to understand. Noise is everything that isn’t signal, with one exception: non-signal content that somehow manages to boost the signal without compromising it in any way is not noise. For example, if we add nonessential elements or attributes to a data visualization to draw the reader’s attention to the message, thus boosting it, without reducing or altering the message in any way, we haven’t introduced noise. No accurate item of data, in and of itself, always qualifies either as a signal or noise. It always depends on the circumstances.

In physics, the signal-to-noise ratio, which is where the concept originated, is an expression of odds: the ratio of the one possible outcome to another. When comparing signal to noise, we want the odds to dramatically favor the signal. Which odds qualify as favorable varies, depending on the situation. When communicating information to someone, a signal-to-noise ratio of 99 to 1 would usually be considered favorable. When hoping to get into a particular college, however, 3-to-1 odds might be considered favorable, but those odds would be dreadful in communication, for it would mean that 25% of the content was noise. Another ratio that is common in data communication, a probability ratio, is related to an odds ratio. Rather than comparing one outcome to other as we do with odds, however, a probability ratio compares a particular outcome to the total of all outcomes. For example, a probability ratio of 85 out of 100 (i.e., the outcome of interest will occur 85% of the time on average), is the mathematical equivalent of 85-to-15 odds. When Edward Tufte introduced the concept of the data-ink ratio back in the 1980s, he proposed a probability ratio rather than an odds ratio. He argued that the percentage of ink in a chart that displays data, when compared to the total ink, should be as close to 100% as possible.

Every choice that we make when creating a data visualization seeks to optimize the signal-to-noise ratio. We could argue that the signal-to-noise ratio is the most essential consideration in data visualization—the fundamental guide for all design decisions while creating a data visualization and the fundamental measure of success once it’s out there in the world.

It’s worth noting that particular content doesn’t qualify as noise simply because it’s inconvenient. Earlier, I said that a signal is the intended message, but let me qualify this further by pointing out that this assumes the message is truthful. In fact, the message itself is noise to the degree that it communicates misinformation, even if that misinformation is intentional. I’ve seen many examples of data visualizations that left out or misrepresented vital information because a clear understanding of the truth wasn’t the designer’s objective. I’ve also witnessed occasions when highly manipulated data replaced the actual data because it told a more convenient story—one that better supported an agenda. For example, a research paper that claims a strong relationship between two variables might refrain from revealing the actual data on which those claims were supposedly based in favor of a statistical model that replaced a great deal of volatility and uncertainty in the relationship, which could be seen in the actual data, with a perfectly smooth and seemingly certain portrayal of that relationship. On occasions when I’ve questioned researchers about this, I’ve been told that the volatility in the actual data was “just noise,” so they removed it. While they might argue that their smooth model illustrates the relationship in a simpler manner, I would argue that it over-simplifies the relationship if they only report the model without also revealing the actual data on which it was based. Seeing the actual data as well helps us keep in mind that statistical models are estimates, built on assumptions, which are never entirely true.

So, to recap, noise in communication, including data visualization, is content that isn’t part of and doesn’t support the intended message or content that isn’t truthful. Turn up the signal; turn off the noise.