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The time complexity for searching in a linked list is O(n).

In computer science, the time complexity of an algorithm quantifies the amount of time taken by an algorithm to run, as a function of the size of the input to the programme. The time complexity for searching in a linked list is O(n), where n is the number of elements in the list. This is because, in the worst-case scenario, the element being searched for could be the last element in the list, or not present at all, which would require traversing through the entire list.

A linked list is a linear data structure where each element is a separate object. Each element, or node, contains a reference (or link) to the next node in the sequence. This structure allows for efficient insertions and deletions, but can be slow to search, as you have to start from the first node and follow the references to find the desired element.

When searching for an element in a linked list, you start at the head of the list and compare the value of each node with the value you're searching for. If they match, you've found the element. If they don't, you move on to the next node. This process continues until you either find the element, or you've checked all the nodes and found that the element isn't in the list.

This means that in the worst-case scenario, you have to check every single node in the list. Therefore, the time complexity is proportional to the number of elements in the list, which is why we say it is O(n). In contrast, if you were searching for an element in an array, and the array was sorted, you could use a binary search algorithm, which has a time complexity of O(log n), making it faster than a search in a linked list for large data sets.

In conclusion, while linked lists have advantages in terms of insertion and deletion, their time complexity for searching is O(n), making them less efficient for this purpose than some other data structures.

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