I come across an explanation of recursion complexity. This screenshot is in question:
How do you get this?
T(n) = 3T(n/4) + n
The $log_n^4$ shown seems to be base 4, and this baffles me. What does the n superscript stand for? I am inclined to think the base of this log should be 3. Can someone explain this to me?
Another example was provided where the tree expands at an exponent of 2:

The formula given is:
T(n) = 2T(n/2) + 2
The log(n) given is said to be of base 2. This makes sense to me, but not the base 4 in given by the first picture.

In this example, we're considering an algorithm that, given a problem of size $n$, does 1 unit of "local" work and divides the remaining work into 3 smaller problems of the same kind, where each of these subproblems has size $n/4$. (Instead of 3 and 4 we could have any numbers; this is just an example.) So if $T(n)$ is the total amount of work done in solving a problem of size $n$, we have the equation $T(n)=3T(n/4)+1$ by assumption.
The tree figure shows a way of thinking about the algorithm and understanding the growth rate of $T$. At $k$ levels below the root, we have $3^k$ subproblems and each subproblem has size $n/4^k$. Since our algorithm stops splitting the problem up when it reaches a base case (i.e. a problem of size 1), the tree has $\log_4n$ levels, and the number of base cases at the bottom level is $3^{\log_4n}$.