Misled by the brain
Our brain is a very powerful information processor and for this it must be used correctly. Because sensory evaluations deal with processing the information obtained from the senses according to subjective experiences, it is necessary to avoid the following errors.
Error of expectation: derives from previous knowledge that the judge may have about the product under evaluation. For example, a trained cheese judge, will tend to “seek” a pungent note (propionic or acetic) in cheeses that have large holes (such as Emmenthaler cheese) as these two elements, the presence of holes and the pungent note, are often related to the production process.
Stimulus error: occurs when the judge is influenced by irrelevant details that “suggest” differences between the products. We all know the example of a group of sommeliers who were asked to describe a white wine sample which was artificially coloured red. The sommeliers were tricked by the colour and perceived the characteristic notes and flavours of red wines that cannot possibly be found in white wines.
Logic error: occurs when the judge uses a logical process for the evaluation of a product. For example, two samples that are coded with the production date or with a percentage value will suggest the judge to evaluate them based on this information. For the same reason, it is not a good idea for judges to know too many details of the samples, the project goals, or why the analysis is being performed.
Contrast and convergence error: these two errors occur when samples that have strong differences are evaluated together. Let’s make an example with cars.
It would not make sense to ask the judges to compare a Ferrari F40 sportscar with a Fiat 500 city car.
Yes, they are both beautiful, they share the Italian origin and the fact that they are, in their respective category, iconic and easily recognisable items. But the evaluation of the two would be meaningless, because the two are so different (for specifications, price, etc.) that any comparison would be of no use. This is a contrast error.
On the other hand, if we added an ugly, unpopular, unsuccessful medium car to the samples, i.e. the Fiat Duna, it would probably have a convergent effect for the Ferrari and the 500, showing more common traits to the latter two cars than there should be by nature.
Halo effect: it is caused by the overlapping of attributes and characteristics of the sample. For example, if we need to evaluate the crunchiness of an apple, the wrinkled appearance of its peel will inevitably affect our evaluation. Or if the apple is particularly appreciated, some of its specific attributes (eg acidity, vegetable aroma, etc.) will also be judged “positively”.