When learning about algorithms in Year 9, it’s important to know about different types of time complexities. This helps us see how quickly an algorithm can work. We use something called Big O notation to show this. Here are some key types:

1. Constant Time - $O(1)$:

   • This means the algorithm takes the same amount of time, no matter how big the input is.
   • Example: Taking a specific item out of a box.

2. Logarithmic Time - $O(\log n)$:

   • With this type, the time goes up slowly as the input size gets bigger.
   • Example: Finding a number in a sorted list using binary search.

3. Linear Time - $O(n)$:

   • Here, the time goes up at the same rate as the input size increases.
   • Example: Going through each item in a list one by one.

4. Quadratic Time - $O(n^2)$:

   • In this case, the time increases based on the square of the input size.
   • Example: Comparing every item in a list with every other item.

Knowing about these time complexities helps students understand how efficient an algorithm is. This knowledge allows them to make better choices when programming.