Noob question: why should we normalize test data with mean and std from training data? • /r/MachineLearning

Well, since both sets are samples from the same distribution, they should ideally have similar means and variances. They obviously won't be identical though, and in this case it makes sense to use the means and variances from the training data, since it's what the model was trained on. The model approximates a mapping from data standardized by the training data's mean and variance, so using the test data's mean and variance would give you inaccurate results.