Time complexity helps us understand how long a computer program will take to run as the amount of information it processes gets bigger. We often talk about this with something called Big O notation. This notation helps us categorize programs based on their worst-case situations.

Key Ideas:

1. Big O Notation: This shows how the time needed for an algorithm is affected as the input grows. Here are some examples:

   • O(1): This means the time stays the same, no matter how much input there is.
   • O(n): This means the time increases directly with the amount of input.
   • O(n^2): This means if the input doubles, the time needed goes up by four times.

2. Input Size: This is simply how many items we're dealing with. For example, if you're looking for a name in a list, the number of names in that list matters a lot.

3. Space Complexity: This measures how much memory a program uses based on the input size. Like time complexity, we also use Big O notation to talk about it.

Knowing these ideas helps us find the most efficient algorithms, which can make our programs run faster and better.