This paper seeks to draw attention to the multiple comparison problem (MCP) within the remote sensing community, and suggest some easily implemented solutions. The use of repeated statistical tests by remote sensing scientists to identify significant relationships, increases the chance identifying false positives (i.e., type-I errors) as the number of tests increases. This paper provides an introduction to the multiple comparison problem (i.e., the impact of the interpretation of p-values when repeated tests are made), outlines some simple solutions, and provides two case studies to demonstrate the potential impact of the problem in empirical remote sensing. The first case study looks at multiple potential texture metrics to predict leaf area index. The second case study examines pixel-wise temporal trend detection. The results show how applying solutions to the multiple comparison problem can greatly impact the interpretation of statistical results.