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The primary purpose of dialogue state tracking (DST), a critical component of an end-to-end conversational system, is to build a model that responds well to real-world situations. Although we often change our minds during ordinary conversations, current benchmark datasets do not adequately reflect such occurrences and instead consist of over-simplified conversations, in which no one changes their mind during a conversation. As the main question inspiring the present study,"Are current benchmark datasets sufficiently diverse to handle casual conversations in which one changes their mind?" We found that the answer is "No" because simply injecting template-based turnback utterances significantly degrades the DST model performance. The test joint goal accuracy on the MultiWOZ decreased by over 5%p when the simplest form of turnback utterance was injected. Moreover, the performance degeneration worsens when facing more complicated turnback situations. However, we also observed that the performance rebounds when a turnback is appropriately included in the training dataset, implying that the problem is not with the DST models but rather with the construction of the benchmark dataset.Preprint. Under review.