Describe a Situation Where You Used Data to Make a Counterintuitive Decision - Bar Raiser Evaluate

Evaluate These Two Answers

"Tell me about a time you challenged an assumption or used data to make a better decision when no one else had noticed the problem."

SDE 2⏱ 3 minAmazon Bar Raiser. LP evaluated explicitly. Content scored, not delivery.

Score BOTH answers on Ownership Signal, Action Specificity, and Quantified Impact BEFORE applying the full rubric.

If you scored Candidate A >40 total, your calibration is biased toward fluency. Bar Raisers ignore delivery and score content only.

Candidate A

My manager suggested I look into this since I had bandwidth. I noticed the issue during a routine review without any assignment and decided to investigate proactively. I discovered a data inconsistency in the user activity logs that was causing inaccurate reporting. I collaborated with the team to identify the root cause and helped deploy a fix. This improved report accuracy by 12%, reducing customer complaints by 8% over the following month.

Fluent delivery, confident tone - most untrained evaluators score this high

Candidate B

I noticed during a routine audit that the user activity logs had inconsistent timestamps, which no one had flagged before and no ticket existed. I challenged the assumption that the logging system was reliable by analyzing raw data and discovered a race condition causing data loss. I independently designed and implemented a fix that improved data accuracy by 15%, reducing customer complaints by 10% over the next quarter. This proactive approach prevented potential revenue loss and improved trust in our analytics pipeline.

35-55 seconds longer - every extra second is signal-dense content

📊

Score Comparison

Dimension

Weight

Candidate A

Candidate B

structure star

15%

10

14

ownership signal

30%

1

28

action specificity

25%

8

24

quantified impact

20%

6

19

self awareness

10%

0

13

Total

25 No Hire

98 Strong Hire

AUTO-FAIL: my manager suggested I look into this since I had bandwidth - assigned task. Score 1. No Hire.

🚨

Auto-Fail Markers

Candidate A implies manager direction

"my manager suggested I look into this since I had bandwidth"

Ownership requires self-initiation. Manager-assigned = execution. Score 1 on ownership_signal (weight=30) = No Hire always.

Candidate A uses collective language hiding individual contribution

"we found a data inconsistency"

Using 'we' hides individual ownership and clarity of contribution, weakening ownership signal. Score 1 on ownership_signal = No Hire.

📝

Bar Raiser Notes

Ownership weak - manager-directed; collective language; zero quantification; No Hire.

🔧

Fix-It Challenge

Ownership clarity

Before"my manager suggested I look into this since I had bandwidth"

→

After"I noticed the issue during a routine review without any assignment and decided to investigate proactively"

Shows self-initiation and ownership rather than manager direction

Individual contribution

Before"we found a data inconsistency"

→

After"I discovered a data inconsistency"

Clarifies personal ownership and impact

Quantification

Before"improved the accuracy of reports, but we did not measure the exact impact yet"

→

After"improved report accuracy by 12%, reducing customer complaints by 8% over the following month"

Adds measurable impact to strengthen the result section

🎓

Coaching Notes

• At Amazon, 'Are Right a Lot' requires candidates to demonstrate self-initiated problem identification and data-driven decision making without manager prompting.
• Avoid collective 'we' language that obscures your individual contribution; use 'I' statements to highlight ownership.
• Quantify impact with metrics and business outcomes to show the significance of your decisions.
• Explicitly challenge assumptions or use data to uncover issues others missed, showing strong judgment.
• Demonstrate self-awareness by reflecting on what you learned or how you improved the process.

⭐

Model Answer Guidance

A strong answer starts with noticing a problem independently, challenges existing assumptions with data, describes specific actions taken by the candidate alone, quantifies the impact with metrics, and ends with a reflection on the broader business effect or learning.