Final answer:
Setting the random state parameter in train_test_split ensures reproducible results and equal chances for participants' group assignments, minimizing systematic differences.
Therefore, the correct answer is: option a). To make experiments easily reproducible by always using the same partitioning of the data.
Step-by-step explanation:
The random state hyperparameter in the train_test_split() function controls the shuffling process.
With random_state = None , we get different train and test sets across different executions and the shuffling process is out of control. With random_state=0 , we get the same train and test sets across different executions.
The purpose of setting the random state parameter in train_test_split is to make experiments easily reproducible by always using the same partitioning of the data.
This allows all participants to have an equal chance of being assigned to either group, ensuring that any differences observed between experimental and control groups result from the manipulation of the independent variable.
The goal of random assignment in experimental design is to minimize systematic differences between groups, and by setting a random state, researchers can ensure that their results are consistent and reliable.