TRT
Sign in
mediumFoundationsconcept

What is the difference between worst-case, average-case, expected and amortized cost?

Define each term, give an example of each, and explain which kind of promise each one makes.

TRT practice prompt — not a verified question from a named employer.

The Runtime Theory Team2 min read
Learn it firstBest, average and worst caseThe same algorithm can take very different numbers of steps on inputs of the same size. Which case you quote decides whether your estimate is a guarantee or a hope.

A strong answer

A strong candidate frames What is the difference between worst-case, average-case, expected and amortized cost? around the exact memory layout, compiler semantics, and hardware execution model covered in Best, average and worst case. Define each term, give an example of each, and explain which kind of promise each one makes.

So far, step counts have been formulas in n alone. For many algorithms that is not enough, because the number of steps also depends on which input of size n they get. The trace above runs the same linear search on the same 8 elements four times: it needs 1, 4, 8 and 8 comparisons, depending only on the target.

The best case is the fewest steps over all inputs of size n. For linear search it is 1, when the target sits at index 0.

In a production interview, always connect the specification rule to its concrete runtime consequence: state what lives on the stack versus the heap or static storage segment, how the compiler or operating system validates alignments, lifetimes, and error codes, and what happens under adversarial or boundary inputs (such as zero-length buffers, integer overflow, concurrent mutation, or partial I/O). Backing your explanation with a small trace of register/memory state transitions immediately separates senior systems engineers from candidates who only memorized syntax.

What the interviewer is listening for

  • Mastery of core invariant: Work out the best, average and worst case of a simple algorithm.
  • Mastery of core invariant: Explain why the worst case is the usual guarantee, and what an average case assumes.
  • Mastery of core invariant: Recognize inputs that push an algorithm into its worst case, including deliberate attacks.
  • Hardware and ABI awareness: Explaining how sizes, alignments, calling conventions, or kernel boundaries behave on real 64-bit targets rather than relying on implementation-defined assumptions.
  • Defensive engineering: Naming the exact compiler flags (-Wall -Wextra -Werror), static analyzers, or runtime sanitizers (-fsanitize=address,undefined,thread) that verify correctness in CI.

Common mistakes

  • Explaining only surface syntax without tracing the underlying memory addresses, ownership lifetime, or kernel/user-space state transitions.
  • Assuming undefined behavior (such as signed overflow, uninitialized reads, strict-aliasing violations, or dangling pointers) will "just work" because it passed an unoptimized -O0 debug build.
  • Ignoring return codes, partial reads/writes, alignment padding, or cleanup paths when an allocation or syscall fails midway through a function.

This answer walks

Practice follow-ups

  1. 01Why is randomized quick sort's O(n log n) a stronger promise than an average over random inputs?
  2. 02A dynamic array append is O(1) amortized. Can a single append still take O(n)?
  3. 03Which of these guarantees would you want for a real-time system, and why?
  4. 04How did hash flooding attacks exploit the gap between average and worst case?

Not started

Sign in to save your learning progress.

Sign in to save