Accessibility Bias

Accessibility Bias

People overvalue information that’s easy to recall. Not because it’s the best data, but because it’s the most available. This bias leads teams to act on familiar inputs rather than investigate what’s actually true or important.


It shows up most when decisions are rushed or when no one questions the first idea that surfaces.

HOW IT SHOWS UP

Design


  • Prioritizing features based on user quotes instead of full patterns

  • Repeating design patterns that are familiar but irrelevant



Product


  • Roadmap items chosen based on what’s trending or loudest

  • Mistaking urgency for importance



Strategy / Leadership


  • Solving symptoms without investigating causes

  • Over-relying on old assumptions or secondhand input

WHEN TO USE THIS MODEL

Spring Planning

This is where time pressure meets half-baked context. Teams default to whatever’s in front of them: the loudest ticket, the easiest fix, or the most familiar ask. If you’re not checking where the request came from or what problem it’s solving, you’re just moving work, not moving forward.


Backlog Grooming

Because it happens often, it’s easy to treat it like a checklist. Items get prioritized without digging into where they came from or if they still matter. People assume if it’s in the backlog, it’s worth keeping. That’s a shortcut. Use this model to audit relevance, not just sort tasks.


Strategy Reviews

Old assumptions stick. Teams overvalue what they’ve seen before or what leadership once said. Nobody questions if that context still applies. This model is a filter: is this direction based on what’s accessible or what’s actually true?


HOW TO APPLY IT

Audit your sources:

Is your data fresh, representative, and relevant—or just memorable?


Surface blind spots:

What voices, inputs, or perspectives are missing?


Slow down to go faster:

A moment spent validating is weeks saved in corrections.


Find signal, not noise:

Ask “What evidence supports this?” and “What might we be ignoring?”


Bring in SMEs:

When information is vague, don’t guess—ask the domain expert.


Audit your sources:

Is your data fresh, representative, and relevant—or just memorable?


Surface blind spots:

What voices, inputs, or perspectives are missing?


Slow down to go faster:

A moment spent validating is weeks saved in corrections.


Find signal, not noise:

Ask “What evidence supports this?” and “What might we be ignoring?”


Bring in SMEs:

When information is vague, don’t guess—ask the domain expert.


Audit your sources:

Is your data fresh, representative, and relevant—or just memorable?


Surface blind spots:

What voices, inputs, or perspectives are missing?


Slow down to go faster:

A moment spent validating is weeks saved in corrections.


Find signal, not noise:

Ask “What evidence supports this?” and “What might we be ignoring?”


Bring in SMEs:

When information is vague, don’t guess—ask the domain expert.


Audit your sources:

Is your data fresh, representative, and relevant—or just memorable?


Surface blind spots:

What voices, inputs, or perspectives are missing?


Slow down to go faster:

A moment spent validating is weeks saved in corrections.


Find signal, not noise:

Ask “What evidence supports this?” and “What might we be ignoring?”


Bring in SMEs:

When information is vague, don’t guess—ask the domain expert.


More Mental Models

More Mental Models

More Mental Models