Title: HOW SAMPLING AFFECTS TRAINING: AN EFFECTIVE SAMPLING THEORY STUDY FOR LONG-TAILED IMAGE CLASSIFICATION. Abstract: The long-tailed image classification problem has been very challenging for a longtime. Suffered from the unbalanced distribution of categories, many deep vision classification methods perform well in the head classes while poor in the tail ones. This paper proposes an effective sampling theory, attempting to provide a theoretical explanation for the decoupling representation and classifier for long-tailed image classification. To apply the above sampling theory in practice, a general jitter sampling strategy is proposed. Experiments show that variety of long-tailed distribution algorithms exhibit better performance based on the effective sampling theory. The code will be released soon later.