The Stubborn Mind: Conservatism Bias and the Art of Changing Your Mind

We were sure the cat wouldn’t jump.

We’d watched her a hundred times. Same shelf, same pause, same cautious tail flick. She never went for the high bookcase. Then one day, coffee in hand and brain on autopilot, we turned our backs—and she launched. Perfect arc. Absolute chaos. Ceramic planters down, pages torn, the universe updated. Except, not really. We caught ourselves saying, “She never jumps.” Even as we wiped soil from the carpet.

We do this all the time. You, us, entire teams. New evidence arrives. Old belief stays seated.

Conservatism bias is the tendency to underweight new evidence and stick with prior beliefs even when the new evidence is strong.

We’re MetalHatsCats, and we’re building an app called Cognitive Biases because we keep meeting this moment in code reviews, product roadmaps, investor updates, even in how we love and how we learn. This article is our field guide—to help you notice the shelf, the jump, and to update fast enough to keep your planters intact.

What Is Conservatism Bias and Why It Matters

The quick, human version

• Your brain builds a story.
• New facts show up at the door.
• You open the door only a crack.

We call it “conservatism” because we conserve our original view. In stats terms, it’s under-updating relative to Bayes’ rule. In life, it looks like “I hear you, but…” stretched across months or years.

Classic experiments show people drag their feet when revising beliefs. In simple tasks where the math is clear, folks still move too little toward new evidence (Phillips & Edwards, 1966; Edwards, 1968). We prefer the comfort of what we already think, even if the data says, “Hey, scoot over.”

Why your brain does this (without villainizing your brain)

• Effort tax – Updating is work. Explaining your old self to your new self takes energy.
• Identity guard – Beliefs tie to identity. Revising feels like a mini-breakup.
• Social glue – Group alignment rewards consistency. Shift too fast, you risk status or trust.
• Noise defense – The world throws garbage data at you. Being cautious isn’t dumb—it’s protective. The bias comes when caution becomes calcification.

Why it matters for real work

• In product – Teams keep shipping features their users don’t love because the roadmap says so.
• In engineering – Incident postmortems identify causes, but the same assumptions survive to break again.
• In finance – Prices move, fundamentals shift, but portfolios linger in yesterday’s weather (Barberis, Shleifer, & Vishny, 1998).
• In health – New research emerges. The diet remains sacred.
• In relationships – People outgrow old patterns. We keep arguing with the version of them from last year.

When you resist updating, you pay compound interest on wrong decisions.

Examples You’ve Probably Lived Through

1. The product roadmap that wouldn’t die. You planned Q2 around a “killer” feature… Conservatism bias muffled it.
2. The incident that repeats itself. An outage happens… It’s still there, in spirit, everywhere.
3. The investor holding too long. You bought a stock on the story of “inevitable turnaround”… Underreaction—classic conservatism in markets (Barberis, Shleifer, & Vishny, 1998).
4. The health habit that never updates. You discovered a diet that once made you feel great… You deferred it.
5. The hiring “type”. Your company hires “rockstars”… Meetings remain full of the same voices.
6. The friendship frozen in time. Your friend used to cancel last minute… Conservatism bias keeps old labels sticky.
7. The coach and the player. A player used to be bad on defense… The season follows the old story, not the new data.
8. The startup that misses a pivot cue. You built a B2C tool… Your revenue begs for “team workflows.”
9. The code that “can’t possibly be the cause”. You’ve been sure the bug is in the new service… The fix was in a configuration you “knew” was harmless.
10. The family rulebook. Growing up, you learned to “finish your plate.”… The belief keeps the door on the chain.

The Core Mechanics (in Plain English)

A Bayes-lite way to think

You start with a belief: “Feature X will increase retention by 5%.” That’s your prior. Evidence arrives: A/B test shows 0.2% lift with high variance. That’s your likelihood. A good update moves your belief a meaningful amount toward the evidence quality. Conservatism means you nudge a millimeter when you should move a mile.

Humans often update too little, even when the data is clear (Edwards, 1968). We’re not robots. But we can borrow robot logic when it helps.

Emotions aren’t the enemy; they’re the dashboard

If an update threatens your identity or social standing, expect drag. Name the fear to defuse the drag:

• “If I change my mind, my team will think I was wrong.”
• “If I drop this feature, months of work look wasted.”
• “If I switch my diet, I admit I’ve been preaching wrong advice.”

Saying the quiet part out loud breaks its spell.

Noise vs. signal: the tricky middle

A single datapoint shouldn’t flip your world. That fear fuels conservatism. The solution isn’t to ignore evidence; it’s to predefine what kind of evidence counts.

• Before running experiments, write your update thresholds.
• If X happens, we do Y. No drama. Just an if-statement for beliefs.

How to Recognize Conservatism Bias in Yourself (and Your Team)

• You say “Let’s wait for more data” without defining what “more” means.
• You move goalposts mid-stream to keep the original plan alive.
• You ask for confirmatory metrics, not disconfirming ones.
• You minimize inconvenient evidence by calling it “an edge case.”
• The bigger the sunk cost, the smaller your update.
• You use “always” a lot about dynamic systems.
• Postmortems end with “add a guardrail” instead of “revisit our assumptions.”
• Stakeholder approval matters more than user behavior.
• Your roadmap changes less than your environment.

The MetalHatsCats Practical Checklist to Counter Conservatism Bias

We use these. They work when we actually stick to them.

Related or Confusable Concepts (And How to Tell Them Apart)

• Confirmation bias – Seeking confirming evidence; conservatism can occur even with fair evidence.
• Status quo bias – Preference for no change; conservatism is under-updating beliefs.
• Anchoring – First numbers overly influence estimates; conservatism is failing to move off the anchor.
• Sunk cost fallacy – Past investments distort choices; conservatism keeps beliefs rigid.
• Belief perseverance – Beliefs survive debunking; conservatism is milder—under-updating.
• Backfire effect – Occasionally corrections strengthen wrong beliefs; conservatism is too little movement.
• Optimism bias & motivated reasoning – Update asymmetrically by valence; conservatism is low sensitivity overall.

A Simple Field Guide: Bayes Without Tears

1. Write your prior in words with a percent.
2. Score new evidence on strength and reliability.
3. Pick an update amount using simple rules (e.g., strong+high → move 20–40 pts).

Bonus: When multiple pieces of evidence arrive, update sequentially and keep notes. Beliefs should look like version history, not a monolith.

How We Build Against This Bias (Stories From Our Studio)

• In standups, anyone can call “under-updating,” and we pause.
• Experiment templates include “What would change our mind?” upfront.
• We track “zombie tasks” and try to kill at least one on Fridays.
• We maintain a “belief backlog” next to the dev backlog.
• In code reviews, we flag assumption-laden comments and ask for sanity tests.