# Doubt in the correctness of decision tree models for constructing a lower bound

If we were to intuitively construct a lower bound for searching an element in a list $A$ containing $n$ integers, it would be in $\Omega(n)$.

But with the decision tree model, the number of leafs is $n$, so we conclude that the lower bound is $\Omega(\log{n})$.

This is the same as finding the maximum element in a list. Intuitively, it is in $\Omega(n)$, but with the decision tree model it is $\Omega(\log{n})$.

Can someone help me understand this discrepancy ?

I challenge you to come up with another argument showing that the depth of a decision tree for finding an element in a list is at least $n$ (or even $n+1$, depending on how depth is defined).