17.2 Big O vs. Worst Case

A short digression on asymptotics

Consider the following statements about BSTs. Which of the following are true?

1. The worst-case height of a BST is $\Theta(N)$.

2. BST height is $O(N)$.

3. BST height is $O(N^2)$.

The answer is that all three statements are true. BSTs always have a height that is linear or better, and a linear height is obviously "less than" the quadratic upper bound in the last point.

However, a more tricky question is which of the three statements is the most informative.

The answer here is the first statement: it gives an exact upper and lower bound unlike the other statements. $O(N)$ could mean linear, logarithmic, square-root, or constant, but $\Theta(N)$ can only mean linear.

For an analogy, consider the following statements about the worst-case cost of a hotel room:

1. The most expensive room is $639/night.

2. The most expensive room is less than or equal to $2000/night.

Here, we see that the first statement gives us exact information, whereas the second statement does not. In the second statement, the most expensive room could be $2000, $10, or anywhere in between.

However, both are statements about the worst case. Applying this to asymptotic notation, this means that we can refer to the worst case with $\Theta$, $O$, or even $\Omega$. Big O is not the same as the worst case!

Using Big O

If $\Theta$ is always more informative than $O$, then why do we bother using Big O notation at all? There are several reasons:

• We can make broader statements. For example, saying "binary search is $O(\log N)$ is correct, but saying "binary search tree is $\Theta(log N)$" would not be correct, since it can be constant in certain scenarios.

• Sometimes, it is not possible or extremely difficult to determine the exact runtime. In such cases, we would still like to provide a generalized upper bound.