What do interviewers evaluate in Are Right, A Lot questions?

Interviewers listen for signals like: Did you reason from the data available at the time, or are you narrating with hindsight bias? Did you actively seek to disconfirm your own view - dissenting opinions, a devil's advocate, contradicting data? Did you state in advance what would change your mind, then actually update when it did?

Amazon LPAdvanced Premium

Are Right, A Lot (Amazon Leadership Principle)

This LP is a trap if you read it as 'I'm always right.' Interviewers screen for strong judgment under uncertainty AND willingness to be disconfirmed.

About this theme

Are Right, A Lot is one of Amazon's 16 Leadership Principles. The principle states that leaders are right a lot, that they have strong judgment and good instincts, and - critically - that they seek diverse perspectives and work to disconfirm their own beliefs. The second half is what most candidates miss. The principle is not about being infallible; it is about having a process that produces correct decisions more often than not, which necessarily includes actively hunting for evidence that you are wrong before you commit. In interviews, this LP is one of the harder ones to answer well because the obvious reading - "tell me about a time you were right" - leads candidates into arrogant, hindsight-biased stories. Skilled interviewers probe the reasoning you used at the moment of decision, when the outcome was still unknown. They want to hear how you weighed limited data, where you sought a dissenting opinion, what would have changed your mind, and how you updated when new information arrived. They will often pair this with a "tell me about a time you were wrong" follow-up to test whether your judgment is calibrated or just lucky. This is typically asked of senior and above candidates.

What interviewers are evaluating

→Did you reason from the data available at the time, or are you narrating with hindsight bias?
→Did you actively seek to disconfirm your own view - dissenting opinions, a devil's advocate, contradicting data?
→Did you state in advance what would change your mind, then actually update when it did?
→Did you make a sound decision under genuine uncertainty, with incomplete information?
→Are your good outcomes the result of repeatable judgment, or were you simply lucky?
→Can you separate the quality of the decision from the quality of the outcome?

Common prompts

Variations on these are asked at every level. Have a story pre-loaded for at least three of them.

?Tell me about a time you made a decision with incomplete information.
?Describe a time your judgment turned out to be right when others disagreed.
?Tell me about a time you had to make a hard call quickly.
?Give an example of a time you were wrong and how you handled it.
?Describe a time you changed your mind based on new data.
?Tell me about a time you relied on instinct or experience rather than data.
?How do you make decisions when the team is split?

Sample STAR answers (preview)

The full library pairs strong and weak examples with notes on what makes each work (or fail). Here's how two of the strong stories open.

STRONG

Strong: Called a risky launch hold, then disconfirmed my own fear

Prompt: "Tell me about a time you made a decision with incomplete information."

Situation: Two days before a major checkout redesign launch, our load test showed p99 latency spiking to 3 seconds under peak load, but only intermittently and only in one of three test runs. The team was split: half wanted to launch on schedule because the spike was inconsistent, half wanted to hold indefinitely. We had real revenue pressure to ship for the holiday season.
Result: We launched on time. - the full Task → Action → Result walkthrough and the "why this works" notes are in the member version.

STRONG

Strong: Was wrong about a database choice and updated fast

Prompt: "Give an example of a time you were wrong and how you handled it."

Situation: I championed moving a high-write analytics service from Postgres to a document store, arguing the flexible schema would speed up our iteration. I was fairly confident based on past experience. We migrated about 30 percent of the workload over six weeks.
Result: The rollback took one sprint because we had migrated incrementally and kept the old path warm. - the full Task → Action → Result walkthrough and the "why this works" notes are in the member version.

What's in the full theme

3 full STAR answers (strong and weak, with interviewer notes) · 6 common pitfalls · 5 follow-up strategies

The full STAR library for this theme is part of Premium

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Every behavioral theme's strong and weak answers, pitfalls and follow-ups
Coach critiques on your own stories (40 a month)

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Related behavioral themes

Dive Deep

Amazon LP

Leaders operate at all levels. The interviewer is testing whether you actually understand your own systems - or whether you summarize what your team built.

Learning from Failure

Microsoft

Microsoft's Growth Mindset core. Also tested at Google, Anthropic, and any company that screens for self-awareness. The signal is whether you actually changed.

Companies that test this theme

Amazon

Senior SDE (L6 / SDE III)

Amazon

Principal SDE (L7)

Practice these stories live

Reading STAR answers is the floor. The interview signal is in delivering them out loud, with follow-ups, under pressure. Write your own Are Right, A Lot story first, then say it out loud in a behavioral mock - Alex reads your story bank.

Write your Are Right, A Lot story →Practice it in a behavioral mock →

Amazon LPAdvanced Premium

Are Right, A Lot (Amazon Leadership Principle)

This LP is a trap if you read it as 'I'm always right.' Interviewers screen for strong judgment under uncertainty AND willingness to be disconfirmed.

About this theme

What interviewers are evaluating

→Did you reason from the data available at the time, or are you narrating with hindsight bias?
→Did you actively seek to disconfirm your own view - dissenting opinions, a devil's advocate, contradicting data?
→Did you state in advance what would change your mind, then actually update when it did?
→Did you make a sound decision under genuine uncertainty, with incomplete information?
→Are your good outcomes the result of repeatable judgment, or were you simply lucky?
→Can you separate the quality of the decision from the quality of the outcome?

Common prompts

Variations on these are asked at every level. Have a story pre-loaded for at least three of them.

?Tell me about a time you made a decision with incomplete information.
?Describe a time your judgment turned out to be right when others disagreed.
?Tell me about a time you had to make a hard call quickly.
?Give an example of a time you were wrong and how you handled it.
?Describe a time you changed your mind based on new data.
?Tell me about a time you relied on instinct or experience rather than data.
?How do you make decisions when the team is split?

Sample STAR answers (preview)

The full library pairs strong and weak examples with notes on what makes each work (or fail). Here's how two of the strong stories open.

STRONG

Strong: Called a risky launch hold, then disconfirmed my own fear

Prompt: "Tell me about a time you made a decision with incomplete information."

Situation: Two days before a major checkout redesign launch, our load test showed p99 latency spiking to 3 seconds under peak load, but only intermittently and only in one of three test runs. The team was split: half wanted to launch on schedule because the spike was inconsistent, half wanted to hold indefinitely. We had real revenue pressure to ship for the holiday season.
Result: We launched on time. - the full Task → Action → Result walkthrough and the "why this works" notes are in the member version.

STRONG

Strong: Was wrong about a database choice and updated fast

Prompt: "Give an example of a time you were wrong and how you handled it."

Situation: I championed moving a high-write analytics service from Postgres to a document store, arguing the flexible schema would speed up our iteration. I was fairly confident based on past experience. We migrated about 30 percent of the workload over six weeks.
Result: The rollback took one sprint because we had migrated incrementally and kept the old path warm. - the full Task → Action → Result walkthrough and the "why this works" notes are in the member version.

What's in the full theme

3 full STAR answers (strong and weak, with interviewer notes) · 6 common pitfalls · 5 follow-up strategies

The full STAR library for this theme is part of Premium

Unlock it with a subscription.

Start Your 7-Day Trial

Try everything free for 7 days. Cancel anytime.

Every behavioral theme's strong and weak answers, pitfalls and follow-ups
Coach critiques on your own stories (40 a month)

View Plans

7-day trial, then $8/mo - or $48/yr ($4/mo, save 50%). Cancel anytime.

Related behavioral themes

Dive Deep

Amazon LP

Leaders operate at all levels. The interviewer is testing whether you actually understand your own systems - or whether you summarize what your team built.

Learning from Failure

Microsoft

Microsoft's Growth Mindset core. Also tested at Google, Anthropic, and any company that screens for self-awareness. The signal is whether you actually changed.

Companies that test this theme

Amazon

Senior SDE (L6 / SDE III)

Amazon

Principal SDE (L7)

Practice these stories live

Write your Are Right, A Lot story →Practice it in a behavioral mock →