Every algorithm you write will behave differently depending on what you feed it. A linear search that finds its target in the first slot does almost no work; the same search looking for a value that isn't there must examine every element. Best-, worst-, and average-case analysis is the vocabulary we use to describe that variation precisely, instead of saying vaguely that an algorithm is "usually fast."
In this module the three cases sit alongside asymptotic notation. The two ideas are related but not the same: a case describes which input you are analyzing, while Big O, Omega, and Theta describe how you bound the resulting cost function. Keeping them separate is one of the most common stumbling blocks in COP3530, and it matters because exam questions, interview questions, and real design decisions all hinge on knowing which case a stated bound refers to.
As you watch, focus on three things. First, how does each speaker characterize the specific input that triggers each case — not just the formula, but the data arrangement that causes it? Second, how is the average case actually computed, since it requires assumptions about input distribution rather than a single example? Third, note which algorithms have three different answers (linear search, bubble sort, quicksort) and which collapse to the same bound in all cases (selection sort, merge sort), and ask yourself why.
A university-style lecture on complexity analysis that treats best, average, and worst case as its central organizing idea. At fourteen minutes it is the longest of the three videos, so expect room for worked examples and step-by-step reasoning about how different inputs lead to different operation counts. It is presented as part of a broader data structures and algorithms series, so the terminology should line up with what a course like this one uses.
This is the most direct match to the topic, covering all three cases in one coherent treatment rather than as a side note, and its lecture format suits the subtopics on defining the cases and identifying them for a given algorithm.
A fast, tightly scripted introduction to analyzing algorithms from Michael Sambol, whose short-form algorithms videos are widely used by CS students. In six minutes it sets up the basic question of how we measure an algorithm's cost and why we reason about input size rather than wall-clock time, and it links out to a companion video on asymptotic notation. Expect code examples used to motivate counting operations.
Sambol's videos are known for accuracy and economy, and this one supplies the analytical framing — what we count and why — that best/worst/average case analysis rests on, while its pairing with an asymptotic notation video reinforces the distinction between case and bound.
A concise explainer on the time complexity of an algorithm from Dinesh Varyani's long-running DSA series, using animated slides to walk through how running time is expressed as a function of input size. With over 150,000 views it is a well-tested introduction, and it supplies the counting-and-simplifying mechanics you need before assigning a complexity to any particular case.
It reinforces the computational core of the topic — deriving a cost expression from code — which is exactly the skill you apply three separate times when producing best-, worst-, and average-case results, and the visual slide format makes the derivations easy to follow and re-watch.
Together these videos build the foundation for MO2 and MO3: they define the three cases, demonstrate how to count dominant operations in loops and searches, and show how to simplify the resulting expressions into standard growth rates for familiar algorithms. Sambol's introduction and its explicit link to asymptotic notation support MO1 and MO5 by keeping the idea of a case distinct from the idea of a bound, which is what lets you justify saying "O for a worst-case guarantee" versus "Theta for a tight bound." Finally, seeing how the three cases diverge across different algorithms prepares you for MO4, where you weigh scalability and practical performance when choosing between candidate algorithms for a given scenario.
These videos were chosen from YouTube on October 2, 2026. Each has captions; the player turns them on. Videos on YouTube can be changed or removed — if one no longer plays, tell your instructor. If a player does not appear, use its Watch on YouTube link.