Randomized algorithms and data structures are often analyzed under the assumption of access to a perfect source of randomness. The most fundamental metric used to measure how "random" a hash function or a random number generator is, is its independence: a sequence of random variables is said to be $k$-independent if every variable is uniform and every size $k$ subset is independent. In this paper we consider three classic algorithms under limited independence. We provide new bounds for randomized quicksort, min-wise hashing and largest bucket size under limited independence. Our results can be summarized as follows. -Randomized quicksort. When pivot elements are computed using a $5$-independent hash function, Karloff and Raghavan, J.ACM'93 showed $O ( n \log n)$ expected worst-case running time for a special version of quicksort. We improve upon this, showing that the same running time is achieved with only $4$-independence. -Min-wise hashing. For a set $A$, consider the probability of a particular element being mapped to the smallest hash value. It is known that $5$-independence implies the optimal probability $O (1 /n)$. Broder et al., STOC'98 showed that $2$-independence implies it is $O(1 / \sqrt{|A|})$. We show a matching lower bound as well as new tight bounds for $3$- and $4$-independent hash functions. -Largest bucket. We consider the case where $n$ balls are distributed to $n$ buckets using a $k$-independent hash function and analyze the largest bucket size. Alon et. al, STOC'97 showed that there exists a $2$-independent hash function implying a bucket of size $Ω( n^{1/2})$. We generalize the bound, providing a $k$-independent family of functions that imply size $Ω( n^{1/k})$.