Your team isn’t just avoiding risk, they’re avoiding the unknown. In this episode of the Cognition Catalog, I break down the ambiguity effect: the cognitive bias that makes unfamiliar ideas, tools, and people feel like threats before anyone’s even evaluated them on their merits.

Have you ever watched a genuinely strong idea get quietly buried in a planning meeting?

Not because anyone argued against it, but because nobody could fully picture how it would play out?

The ambiguity effect is what happens when our brains treat “I don’t know” as “probably bad.” It’s not the same as avoiding risk. Risk is when you know the odds, and they might not be in your favor. Ambiguity is when you don’t even know the odds, and that uncertainty is what our brains treat as a genuine threat. The research goes back to economist Daniel Ellsberg in 1961, who ran a simple thought experiment with two jars of colored balls and predicted that people would almost always choose the jar they could see clearly, even when the hidden jar was, on average, an equally good bet. Decades of follow-up research confirmed he was right.

In a UX or product context, this shows up constantly and quietly. It’s the familiar roadmap feature that beats out the bolder, fuzzier idea. It’s the proven engineering framework that wins over the newer tool that might actually be a better fit. It’s the new hire or new leader whose ideas get extra scrutiny, not because they’re weaker, but because you haven’t had enough time to read them yet. The bias doesn’t announce itself. It disguises itself as discipline and caution. In this episode, I walk through where the ambiguity effect comes from, how to spot it in your team’s decision-making, and a handful of practical ways to make sure your best ideas get a fair shot before the fog scares everyone off. Give it a listen.fds

Topics:
• 02:28 – How the ambiguity effect plays out in a planning meeting
• 03:59 – What the ambiguity effect actually is
• 05:00 – The Ellsberg experiment
• 07:05 – Risk vs. ambiguity, what’s the difference?
• 08:22 – How it shows up on your roadmap
• 09:49 – When the unknown is a person
• 12:10 – What to do about it
• 14:42 – Closing thought

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Your brain is wired to find patterns. That's mostly a good thing... until it's not. This episode breaks down illusory correlation: why your team sees connections that aren't there, and what you can do to stop making decisions based on a handful of vivid moments dressed up as a trend.

Have you ever watched your team make a confident product decision based on a pattern that, when you actually look at the data, barely exists?

Illusory correlation is the bias that turns coincidence into conviction. When two things happen close together -- even just once or twice -- our brains quietly file them as connected. The concept was first identified by psychologist Loren J. Chapman in 1967, who noticed that trained clinical professionals were reporting patient behavior patterns that statistically didn't exist. The problem isn't laziness or bad intent. It's just how human memory works. Rare or distinctive events get stored differently, and when two unusual things co-occur, the brain treats that pairing as meaningful -- even when it's pure chance.

In product and design work, this plays out constantly and in ways that feel completely legitimate. A feature ships and traffic ticks up the next day, so the launch gets the credit -- even though a competitor was down and marketing ran a campaign. Six user interviews produce two mentions of a feature, and suddenly that feature defines the whole persona. A few support tickets from one customer segment, and that segment becomes "a tough audience." The misses get forgotten. The hits stack up. And the team ends up navigating by a pattern that was never really there. This episode breaks down how illusory correlation sneaks into your metrics, your research, and your team dynamics -- and gives you a few concrete habits to start catching it before it shapes your roadmap. Give it a listen.

Topics:
• 02:20 – Personal story: the engineering lead I had all wrong
• 04:29 – What is illusory correlation?
• 04:46 – The origin: Chapman’s 1967 research
• 06:19 – Hamilton & Gifford: how the bias distorts how we see groups
• 07:10 – Kahneman & Tversky: why illusory correlations stick
• 07:50 – How it shows up in your product metrics
• 08:23 – The A/B testing problem
• 09:00 – How it distorts how teams think about people and segments
• 09:26 – How it corrupts user research
• 09:50 – Engineering superstitions and team dynamics
• 10:27 – Why more data isn’t always the fix
• 11:00 – Five habits to fight illusory correlation

—

Thanks for listening!

We hope you dug today’s episode. If you liked what you heard, be sure to like and subscribe wherever you listen to podcasts! And if you really enjoyed today’s episode, why don’t you leave a five-star review? Or tell some friends! It will help us out a ton.

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• ⁠⁠⁠⁠Get a FREE audiobook AND support the show⁠⁠⁠⁠

• ⁠⁠⁠⁠Support the show on Patreon⁠⁠⁠

⁠⁠⁠⁠⁠⁠⁠• ⁠⁠⁠⁠Check out show transcripts⁠⁠⁠⁠

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