Distinction bias was first described by researchers who wanted to understand how context can influence our decisions. The concept emerged from studies within consumer behavior research. These studies showed that when individuals assess products side by side, they tend to focus more on contrasting features rather than on the overall value or suitability of the options on their own.

The term “distinction bias” itself was coined by behavioral economists who were investigating why people often make choices that they later regret. Their research demonstrated that the context in which we evaluate options plays a crucial role in our decision-making processes. By examining various decision-making scenarios, they found that the side-by-side comparison of options often led to an exaggerated perception of differences.

As the understanding of distinction bias grew, researchers explored its implications across various domains, including marketing, policy-making, and personal finance. In marketing, for example, companies learned that presenting products in a way that highlights their distinct features could influence consumer preferences and increase sales. Policymakers recognized that presenting policy options with stark contrasts could sway public opinion, even if the differences were minor in the grand scheme of things. In personal finance, advisors began to caution clients against making investment decisions based on exaggerated distinctions between similar options.

For software teams, distinction bias can affect how teams evaluate and prioritize features. During product development, when comparing different versions of a product or feature, team members might fixate on minor design or performance differences, potentially overlooking more significant aspects such as the overall experience or value. For instance, the team might compare two iterations of a feature side by side and focus too much on visual tweaks rather than functionality.

For engineers, distinction bias can influence the selection of technologies, frameworks, or libraries. When comparing different solutions, engineers might overvalue minor performance differences and underestimate other critical factors such as scalability, maintainability, or support. For example, choosing between two frameworks based solely on a small speed difference might lead to a decision that doesn’t consider the broader implications for the project.

This type of thing happens on software teams all the time when comparing off-the-shelf solutions for everything from frameworks, and component libraries to even tools to help manage and run your team.

Don’t fall for it!