What does it mean if an algorithm has linear time complexity?

When an algorithm has linear time complexity, it means that its running time increases directly in proportion to the size of the input. This implies that as the size of the input data grows, the time taken to run the algorithm increases predictably and linearly. For instance, if doubling the input size doubles the running time, then the algorithm exhibits linear time complexity.

This proportional relationship allows for straightforward predictions and analysis regarding performance; for example, if the algorithm processes 100 elements in 1 second, you can expect it to take around 2 seconds for 200 elements. Linear time complexity is often denoted as O(n), where n represents the number of input elements.

Understanding this concept is crucial in algorithm analysis, as it helps in evaluating an algorithm's efficiency and scalability against larger datasets.