  Images are at the core of most modern biological experiments and are used as
a major source of quantitative information. Numerous algorithms are available
to process images and make them more amenable to be measured. Yet the nature of
the quantitative output that is useful for a given biological experiment is
uniquely dependent upon the question being investigated. Here, we discuss the 3
main types of visual information that can be extracted from microscopy data:
intensity, morphology, and object counts or categorical labels. For each, we
describe where they come from, how they can be measured, and what may affect
the relevance of these measurements in downstream data analysis. Acknowledging
that what makes a measurement "good" is ultimately down to the biological
question being investigated, this review aims at providing readers with a
toolkit to challenge how they quantify their own data and be critical of
conclusions drawn from quantitative bioimage analysis experiments.
