Terminology  Called As Definition
O(1) Constant Time Time is taken for an operation, fixed number of steps
E.g. Push and Pop operations for the stack.
Enqueue and Deque operations for Queue.
O(log n) Logarithmic Time Time was taken will double with each additional element in the input data set
E.g. Binary Search
Insert and Find operations in a Binary Search tree.
Insert and Remove operations for a heap
O(n) Linear Time A number of steps proportional to the size of the tasks.
(If the size of the task increases the no of steps increases)
E.g. Finding Max/Min element in a list.
Sequential search in an unsorted list of n elements.
Traversal of a tree with n nodes.
Calculating iteratively n-factorial.
Finding iteratively the nth Fibonacci number
O(n log n) Linean Arithmetic

You are performing an O(log n) operation for each item in your input.

Also called as Log-Linear, or QuasiLinear

Most (efficient) sort algorithms are an example of this.
O(n log n) . "n log n" time
E.g. Quick Sort, Merge Sort
O(n2) Quadratic Time The number of operations is proportional to the size of the task squared.
E.g. Selection Sort of n elements.
Comparing two-dimensional arrays of size n by n.
Finding duplicates in an unsorted list of n elements
(Implemented with two nested loops)
2O(n) Exponential Time Which is common for artificial intelligence tasks and is really quite bad. Exponential-time algorithms begin to run the risk of having a decent-sized input not finish before the person wanting the result retires.
O(n!) Factorial time  


Run time matrix for each Big O Term:


Input Constant Logarithmic Linear Linear Arithmatic Quadratic Cubic
N O(1) O(log N) O(N) O(N log N) O(N2) O(N3)
1 1 1 1 1 1 1
2 1 1 2 2 4 8
4 1 2 4 8 16 64
8 1 3 8 24 64 512
16 1 4 16 64 256 4,096
1024 1 10 1,024 10,240 1,048,576 1,073,741,824