Time complexity is a way to see how long an algorithm will take to run, especially when we give it more data to work with.

Here are some common types of time complexities written in a simpler way, using Big O notation:

1. Constant Time - O(1):
   This means the algorithm takes the same amount of time no matter how much data you have.
   For example, finding the first item in a list is always quick!

2. Linear Time - O(n):
   In this case, the time it takes grows just like the size of the data.
   So, if you need to look at each item in a list, it will take longer if the list is bigger.

3. Quadratic Time - O(n²):
   Think of this like having two loops. If you check each item against every other item, the time can grow really fast.
   If you double the size of the list, it could take four times longer!

4. Logarithmic Time - O(log n):
   This is a very efficient way to find things!
   Imagine searching in a sorted list by cutting the size in half over and over again, like in binary search.

Knowing these types of time complexities helps you choose the best algorithms for your tasks!