What is the difference between time and space complexity?

Time and space are separate budgets. An algorithm can be fast but use a lot of memory, or use very little memory but recompute the same values repeatedly. Interviewers care about both because production systems have limits on latency and on memory.

function fibRecursive(n) {
  if (n <= 1) return n;
  return fibRecursive(n - 1) + fibRecursive(n - 2);
}

function fibMemo(n, memo = new Map()) {
  if (n <= 1) return n;
  if (memo.has(n)) return memo.get(n);
  const value = fibMemo(n - 1, memo) + fibMemo(n - 2, memo);
  memo.set(n, value);
  return value;
}

How to talk about the trade-off

• Count extra space, not the input itself, unless the input is copied
• Recursion uses stack space proportional to call depth
• Hash maps and DP tables use memory proportional to their entries
• In-place algorithms save space but may mutate the input

A useful answer names the bottleneck and the constraint. If memory is tight, prefer an in-place approach or a rolling window of values. If latency is the priority, caching or precomputation may be worth the memory.