Implement min, mean, median robustly

Q: Implement min, mean, median robustly

This question evaluates implementation skills in basic descriptive statistics (min, mean, median), numerical stability in online aggregation methods, robust handling of edge cases like None/NaN and extreme numeric ranges, and algorithmic complexity reasoning in the Coding & Algorithms domain for a Data Scientist role.

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Question

Implement three functions in Python without using numpy/pandas: (1) my_min(nums) returning the minimum in O(n) time and O(1) space; (2) my_mean(nums) using a numerically stable online method (e.g., Kahan or Welford) and explaining why naive summation can be inaccurate; (3) my_median(nums) for both odd and even lengths. First provide an O(n log n) solution using sorting, then implement an O(n) expected-time solution using quickselect; discuss behavior for empty lists, presence of None/NaN, and very large integers/floats; analyze time/space complexity and worst-case pitfalls.

Implement min, mean, median robustly

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