When you hear about Big O notation, it may sound like an intimidating concept reserved for mathematicians and computer scientists. However, it’s one of the most important tools for evaluating how efficiently algorithms run as the size of their input grows. In this guide, we’ll break down the most common types of Big O time complexities, explain them in simple terms, and provide Kotlin code examples that you can run in the Kotlin Playground to better understand each concept. What is Big O Notation? Big O notation describes how the runtime or space requirements of an algorithm grow relative to the input size. It’s a way to compare the efficiency of different algorithms regardless of hardware or implementation details. Big O focuses on worst-case scenarios to help predict the performance of an algorithm. Common Types of Big O Here are the most common types of Big O complexities and what they represent: O(1) — Constant Time Growth: Runtime remains the same rega...