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Data scientists · analystsStatistics · experimentation · ML fundamentals

Amazon · Data Scientist

Hypothesis testing, experiment sizing, and quality metrics for the data-science loop — the statistics Amazon asks before the ML questions.

3 phases · 26 problems · 26 unique

Start timed mock5 problems · 4 min each

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1Warm-up

0/8

Mean
st-001Statistics
Easy
Variance
st-002Statistics
Easy
Median
st-006Statistics
Easy
Median Absolute Deviation
st-016Statistics
Easy
Z-Score
st-018Statistics
Easy
Standard Error of the Mean
st-019Statistics
Easy
Accuracy
ml-008ML Fundamentals
Easy
Confusion Matrix Counts
ml-016ML Fundamentals
Easy

2Core

0/10

Covariance
st-003Statistics
Medium
Pearson Correlation
st-004Statistics
Medium
One-Sample t-Statistic
st-037Statistics
Medium
Pooled Variance
st-038Statistics
Medium
Chi-Square Statistic for Counts
st-039Statistics
Medium
Odds Ratio and Risk Ratio 2x2
st-040Statistics
Medium
Precision, Recall, F1
ml-009ML Fundamentals
Medium
R-Squared Score
ml-020ML Fundamentals
Medium
AUC by Trapezoid
ml-074ML Fundamentals
Medium
Statistical Power
ml-187ML Fundamentals
Medium

3Hard

0/8

Bootstrap Confidence Interval
ml-050ML Fundamentals
Hard
Two-Proportion Z-Score
ml-184ML Fundamentals
Hard
Benjamini-Hochberg Count
ml-193ML Fundamentals
Hard
Welch t-Statistic
st-044Statistics
Hard
G-Test Statistic
st-046Statistics
Hard
Permutation p-Value from Difference of Means
st-047Statistics
Hard
Mann-Whitney U and Wilcoxon Rank-Sum
st-049Statistics
Hard
Cramer's V from Chi-Square
st-050Statistics
Hard