If the math class selects students for the advisory panel truly at random, without any bias or manipulation, then the data is not biased.
If the math class's process for selecting two students for the advisory panel is genuinely random and free from biases or manipulation, then it can be considered non-biased. True randomness ensures each student has an equal chance of being chosen, minimizing the potential for favoritism or discrimination.
However, the key is to scrutinize the implementation of the random selection to confirm its fairness. If the process adheres strictly to random principles, it provides a level playing field and supports the conclusion that the data is not biased.