Decision Science News ran a post about communicating complex scientific topics to the general public. Their solution? Use pictures.
The original blog post is fairly dense, but here are a few representative sections:
What may seem unambiguous is actually interpreted by different people in different ways. A survey of people in 5 international cities found no agreement on what a 30% chance of rain means. Some thought it means rain on 30% of the the day’s minutes, others thought rain in 30% of the land area, and so on. A further problem with the statement is that it gives no information about what it means to rain. Does one drop of rain count as rain? Does a heavy mist? Does one minute of rain count?
There might be a clearer way to state this. Technically, percentage of precipitation refers to the likelihood that there will be some rainfall at any particular spot, throughout the course of time covered by the forecast. But wow, that’s a mouthful!
Compare this with another example, cribbed from the Decision Science News site. This is a style of question given in medical schools to help doctors explain tests to their patients:
The probability of esophageal cancer in a certain population is 1%. If a person has esophageal cancer, the probability that the haemoccult test is positive is 7%. If a person does not have esophageal cancer, the probability that he still tests positive is about 86%. What is the probability that a person from the population who tests positive actually has esophageal cancer?
If your head is swimming in numbers, you are not alone. Here’s a simpler version:
In other words, only one out of every hundred people have cancer. If we run the test, about 7 times out of a 100 the test will be positive. But this particular test isn’t very reliable, so most of those are actually false positives. In this example, testing positive for cancer just once is not a cause for major alarm.
Some more examples from the text show just how confusing it can be to those who aren’t experts in the subject area.
In addition, when risks are described as probabilities, people tend to overweight small probabilities and underweight large probabilities. This observation shows up in the “probability weighting function” of Tversky & Kahneman’s Prospect Theory, the dominant behavioral model of gamble evaluations. A representation that leads to misperceptions of underlying probabilities is undesirable.
Out of every 10,000 people, 30 have colorectal cancer. Of these 30, 15 will have a positive haemoccult test. Out of the remaining 9,970 people without colorectal cancer, 300 will still test positive. How many of those who test positive actually have colorectal cancer?
In the case studied, among women receiving mammographies 3 in 1000 died of cancer, while among women not receiving mammographies 4 in 1000 died of this cause. The absolute risk reduction pops out of this formulation, and we see it to be 1 in 1000.
The important thing to take away from this is how much simpler the above diagram makes the data. When you talk in percentages things can get very confusing, especially when the actual content being covered is confusing. A clear, concise diagram can do wonders for clarity.
When things are difficult to comprehend, it’s always worth your time to see if a diagram could clear things up. Even if something is relatively simple, a diagram can ensure that everyone is on the same page about the information being presented. Confusion is one of the biggest inhibitors of success in many organizations. Clear communication isn’t always easy, but it is always beneficial and necessary. Any tool you can use to increase understanding is one you should constantly utilize.
We’ve written about the power of diagrams many times before. Complex ideas can often be explained simply and quickly using pictures. Contact our business process improvement methodology specialists today to learn more!