Communicating Numbers - Group Activity

A tendency to search for, favor, and interpret information in a way that validates pre-existing beliefs while ignoring evidence that contradicts them.
→ Find some techniques to break confirmation bias gently?
Example: A user searching the internet for “benefits of chocolate for weight loss” while ignoring all medical articles that state the opposite.

Using visual properties like color or layout that carry strong cultural associations, leading the brain to assume a relationship that is not in the data.
→ What cultural associations can you think of and how to spot, avoid or handle them?
Example: Using green for “Population Growth” and brown for “Population Decline”; readers may mistakenly assume the brown areas represent rural or farming land rather than just a numeric decrease.

The tendency for an audience to draw different conclusions from the same data depending on how it is presented, such as emphasizing gains versus losses.
→ What are ways to present data / findings more objectively?
Example: Reporting that a surgery has a 90% success rate sounds more positive to a patient than reporting it has a 10% failure rate.

Presenting data either too broadly—which hides important variance—or with too much granular detail, which creates “visual noise” and hides the main message.
→ How to balance information overload and vagueness?
Example: A political forecast showing a candidate has a “3% chance of winning” as a single headline, which hides the complex distribution of possible election outcomes from the public.

The phenomenon where experts, who understand a topic intimately, overestimate how much of their mental model is shared by their audience.
→ What can the expert do to avoid going over the audiences heads?
Example: A math professor goes over concepts and uses specialized terms without explanation leaving students scratching their heads.

Using visual channels—such as size, orientation, or color—that do not align with the audience’s internal mental model of how that concept works.
→ What might be the causes and remedies for such misunderstandings?
Example: A novice user looking at a tree map for the first time and failing to understand that the area of the boxes represents a specific numeric value.

A tendency to intentionally overlook or avoid information that is psychologically uncomfortable or contradicts a desired outcome.
→ How to overcome hazardous ignorance in yourself and/or in the audience?
Example: An individual avoiding looking at their bank statement during a month of high spending because the information is stressful.

Presenting a “statistical average” as a representative midpoint when, in reality, it may correspond to no actual individual in the dataset.
→ How to set the average in context or what can you report instead?
Example: The 1940s U.S. Air Force designing cockpits for the “average pilot” measurements; when they actually measured 4,000 pilots, they found that zero individuals actually fit those average dimensions.