The worst case time complexity of AVL tree is better in comparison to binary search tree for

The worst-case time complexity for operations like search, insert, and delete in an AVL tree is O(log N), where N is the number of nodes in the tree. This is because AVL trees are self-balancing binary search trees, maintaining a balance factor (the height difference between the left and right subtrees) that ensures the tree remains balanced after every operation. As a result, the height of the tree is kept logarithmic relative to the number of elements, ensuring operations can be performed in logarithmic time.

In comparison, for a regular Binary Search Tree (BST) that is not self-balancing, the worst-case time complexity for these operations can degrade to O(N) in scenarios where the tree becomes unbalanced and takes the form of a linked list (for example, when elements are added in sorted order). This significant difference in time complexity makes AVL trees more efficient than unbalanced BSTs for scenarios where the tree is expected to be modified frequently, ensuring fast lookup, insertion, and deletion times regardless of the order data is inserted or deleted.

Hence, the statement is true: The worst-case time complexity of AVL tree operations is better (more efficient) in comparison to those in a (non-self-balancing) binary search tree.