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Naive recursion is highly inefficient because it generates exponentially many repeated subproblems. When visualized as a recursion tree, identical nodes such as $f(4)$, $f(3)$, and $f(2)$ are triggered multiple times by different parent nodes, meaning the same calculations are redundantly executed across different branches.

Conditions: The algorithm uses naive recursion without memoization.; The recurrence relation is $f(n) = f(n-1) + f(n-2)$.

Naive recursion is highly inefficient because it generates exponentially many repeated subproblems. When visualized as a recursion tree, identical nodes such as $f(4)$, $f(3)$, and $f(2)$ are triggered multiple times by different parent nodes, meaning the same calculations are redundantly executed across different branches.

Conditions: The algorithm uses naive recursion without memoization.; The recurrence relation is $f(n) = f(n-1) + f(n-2)$.

The number of nodes per level grows as a power of 2 because each recursive call splits into exactly two subcalls. Starting with 1 node at the root (level 1, $2^0$), the second level has 2 nodes ($2^1$), the third has 4 nodes ($2^2$), and the fourth has 8 nodes ($2^3$).

Conditions: The recurrence relation is $f(n) = f(n-1) + f(n-2)$, producing a binary tree.; Levels are numbered starting from 1.; The tree is counted as a full binary tree for theoretical estimation.

The number of nodes per level grows as a power of 2 because each recursive call splits into exactly two subcalls. Starting with 1 node at the root (level 1, $2^0$), the second level has 2 nodes ($2^1$), the third has 4 nodes ($2^2$), and the fourth has 8 nodes ($2^3$).

Conditions: The recurrence relation is $f(n) = f(n-1) + f(n-2)$, producing a binary tree.; Levels are numbered starting from 1.; The tree is counted as a full binary tree for theoretical estimation.