When choosing sorting algorithms for projects, using Big O notation can be confusing and too simple. While it helps us understand how algorithms perform, there are important things students need to know:

1. Generalization Problems: Big O notation usually shows the worst-case scenario. This might not represent how algorithms really work in average or best cases. For example, QuickSort is $O(n \log n)$ on average, but in the worst case, it can turn into $O(n^2)$. This difference might not be clear if you only look at Big O.

2. Constant Factors: Big O notation ignores constant factors and smaller terms. Two algorithms can both be $O(n \log n)$, but one might actually be slower because it has a bigger constant factor when working with smaller datasets.

3. Data Characteristics: How well sorting algorithms work can depend on the type of data being sorted. For instance, Insertion Sort is really good for data that is almost sorted and can work in $O(n)$ time. Many students forget that the type of data can change how well an algorithm performs.

4. Space Complexity: Big O notation often focuses on how fast an algorithm runs, but how much memory it uses (space complexity) is also important. Some algorithms, like MergeSort, need extra space, which can be a problem if you're low on memory.

To help with these issues, students should:

• Test the algorithms: Try out different algorithms and see how they perform on real datasets. Testing can reveal things that Big O analysis doesn’t show.
• Look at average performance and constant factors: Understanding how algorithms behave in average situations and considering constant factors can help.
• Examine the specific situation: Knowing the type of data can help pick the best sorting algorithm for the job.

In short, while Big O notation is a helpful tool for analyzing performance, relying on it too much can lead students to make poor decisions for their projects.