  Validity is the truth of an inference made from evidence, such as data
collected in an experiment, and is central to working scientifically. Given the
maturity of the domain of music information research (MIR), validity in our
opinion should be discussed and considered much more than it has been so far.
Considering validity in one's work can improve its scientific and engineering
value. Puzzling MIR phenomena like adversarial attacks and performance glass
ceilings become less mysterious through the lens of validity. In this article,
we review the subject of validity in general, considering the four major types
of validity from a key reference: Shadish et al. 2002. We ground our discussion
of these types with a prototypical MIR experiment: music classification using
machine learning. Through this MIR experimentalists can be guided to make valid
inferences from data collected from their experiments.
