A model learns the patterns present in its training data. If the data is skewed, thin, or mislabeled, the model learns the skew and reports it back with the same confident tone it uses for a sound pattern. The diagram shows the mechanism: a training set that overrepresents one group produces a prediction that is accurate for that group and misleading for everyone else.
Consider a genetic study built mostly from people of one ancestry. The patterns the model learns reflect that group's DNA. Applied to a different population, the same model can rank the wrong genes highly, not because the biology differs, but because the training examples did. The model has no way to know it is outside its experience.
Thin data causes a related failure. If only a handful of examples exist for a rare disease, the model may latch onto whatever coincidental features those few examples share. The pattern is real in the data and meaningless in biology.
Mislabeled data is the quietest problem. If some training examples carry the wrong outcome — a sample recorded as diseased when it was not — the model learns from the error without any signal that it is wrong. Nothing in the output flags it.
The consequence is that a prediction's reliability depends on the data behind it, and that dependency is invisible in the output. A score of ninety percent looks the same whether it rests on a million well-balanced examples or a hundred skewed ones. This is why data quality is not a preliminary chore to get past; it is part of the result.