I knew Fisher-Yates going in so the code itself came out fine.
Start by clarifying the requirements and edge cases, then implement the Fisher-Yates (Knuth) shuffle iterating from the last element down to the second, swapping each with a randomly chosen index in [0, i]. Explain why this yields a uniform permutation by showing each element has an equal probability of landing in each position, and analyze time and space complexity.
Pro tip: Mention that using Math.random() is acceptable for most interviews but note its limitations (e.g., not cryptographically secure) and that for production code you might use crypto.getRandomValues for better randomness. Also, emphasize that the shuffle is in-place and modifies the input array, which is often expected but worth confirming.
Confirm that the function should shuffle in-place, return the array (or undefined), and handle empty or single-element arrays gracefully. Ask if the input can be mutated or if a copy is needed.
Select Fisher-Yates (Knuth) shuffle for O(n) time and O(1) space. Explain that iterating from the end and swapping with a random index in [0, i] ensures each permutation is equally likely.
Write clean JavaScript code: loop i from arr.length - 1 down to 1, generate j = Math.floor(Math.random() * (i + 1)), and swap arr[i] and arr[j]. Include a guard for arrays with length <= 1.
Explain that for each i, the probability of any remaining element being placed at position i is 1/(i+1). By induction, each of the n! permutations has probability 1/n!, so the shuffle is uniform.
State O(n) time and O(1) extra space. Walk through a small example (e.g., [1,2,3]) to verify correctness, and mention testing with edge cases like empty array and single element.
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