Beyond Bias & Variance

Considering the “Invisible Gap” between Measurements and their Meaning

In an era of empiricism, where insights that are “data-driven” are automatically deemed superior, quantification is vital. Indeed, the measurement of constructs and phenomena is at the core of empirical science, research, and reasoning. However, this quantification is often challenging, and is interrogated accordingly during the research process. Nevertheless, the criteria for “good” quantification are arguably agreed upon; These goals are the minimization of bias and variance.

During a visit …

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