The time complexity of a linear search algorithm is O(n), where n is the size of the input array. In a linear search, the algorithm iterates through each element of the array until it finds the target element or reaches the end of the array. In the worst-case scenario, the target element is located at the last position or is not present at all. This means that the algorithm needs to traverse the entire array, resulting in a linear relationship between the input size and the time needed to perform the search.

  Regardless of the actual distribution of the data in the array, a linear search algorithm always needs to examine each element sequentially until it reaches the desired element or exhausts the entire array. As a result, the time complexity is proportional to the input size.

  In terms of Big O notation, O(n) represents a linear time complexity, indicating that the time required by the algorithm increases linearly with the size of the input. This is the simplest and most basic search algorithm, suitable for small or unordered arrays. For larger or sorted arrays, more efficient algorithms like binary search, hash tables, or binary trees should be used to achieve better time complexities.