Recently there has been a growing interest in fairness-aware recommender\nsystems including fairness in providing consistent performance across different\nusers or groups of users. A recommender system could be considered unfair if\nthe recommendations do not fairly represent the tastes of a certain group of\nusers while other groups receive recommendations that are consistent with their\npreferences. In this paper, we use a metric called miscalibration for measuring\nhow a recommendation algorithm is responsive to users' true preferences and we\nconsider how various algorithms may result in different degrees of\nmiscalibration for different users. In particular, we conjecture that\npopularity bias which is a well-known phenomenon in recommendation is one\nimportant factor leading to miscalibration in recommendation. Our experimental\nresults using two real-world datasets show that there is a connection between\nhow different user groups are affected by algorithmic popularity bias and their\nlevel of interest in popular items. Moreover, we show that the more a group is\naffected by the algorithmic popularity bias, the more their recommendations are\nmiscalibrated.\n