When the Result Doesn't Speak for Itself | Visual AI Learning Guide

Now the target is set, and the loop starts producing results. Some of them land right where you expect. Others sit far outside the band, and the number alone will not tell you why.

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Once a target is set, the loop produces results — and some of them are ambiguous. A measurement can sit outside the expected band because the compound genuinely did something interesting, or because the run failed. The number alone cannot separate those two cases. A human looks at the surrounding evidence, the controls, and the history, and decides whether to trust the result, repeat it, or discard it. Only after that judgment does the loop continue.