The bilateral trade data provided by the United Nations International Trade Statistics Database are some of the most authoritative trade statistics and have been widely used in many research fields. Here, we propose a new form of inconsistency in its records, namely statistical imbalance, which refers to the phenomenon of inequality between the import or export trade value of a commodity category and the total value of all its subcategories. We investigated the frequency and spatial-temporal patterns of the statistical imbalances of 15 reporters (i.e., Australia, Brazil, Canada, China, France, Germany, India, the Netherlands, the Rep. of Korea, the Russian Federation, Switzerland, the United Arab Emirates, the United States of America, and Vietnam) from 1996–2016 and explored their distributional differences in commodity categories with a co-clustering algorithm. The results show that statistical imbalance is widespread with obvious clustering patterns. Trade records related to specific categories such as fossil fuels, pharmaceuticals, machinery, and unspecified commodity categories presented severe statistical imbalances, which may lead to erroneous trade research results. Since statistical imbalance is difficult to detect in studies focusing only on specific commodity categories, we suggested that researchers should prescreen the data for statistical imbalance to ensure the validity of their results.