Our experiment involved seven panels and six chocolates – five dark chocolates and one milk chocolate. The aim of the study was to compare the sensory profiles of the chocolates. A natural question to ask is "Did the panelists detect any differences among the five dark chocolates or did they systematically contrast them with the milk chocolate?" The scatter plot of the chocolates obtained by principal component analysis was useless to answer that question, because of the proximity of the points. To overcome that, we used confidence ellipses calculated using bootstrap. The originality of the study lies in the fact that we applied those ellipses to hierarchical multiple factor analysis (HMFA): among the seven panels, six were composed of trained professionals and the last one was composed of untrained students, and through that method, we managed to compare the two types of panels and balance the role of each trained panel. HMFA provides in a single scatter plot a representation of the six chocolates for each panel, the trained panels and all the panels. Confidence ellipses around each chocolate show that the combined panels – the six trained panels and also the untrained panel – differentiate the five dark chocolates. They also show how much larger the untrained panel's variability is than that of the trained panels, and how comparable are the trained panels' variability to each other.