A
meta‑statistical framework arguing that any statistical claim is only meaningful relative to a specific set of parameters (what is measured, how, at what scale) and a specific reference frame (the population,
time period, and context). Changing either can change the result entirely,
yet statistics are often presented as absolute. This theory demands that every statistical assertion be accompanied by its parameters and reference frame, and that comparisons across frames be
made with extreme caution. It explains why contradictory studies can both be “
true” – they operate in different frames.
Theory of Parameters and Reference Frames of Statistics Example: “He quoted a crime statistic without mentioning it was from 1990, in a small town, counting only reported felonies. The theory of parameters and reference frames reminds us: no statistic is context‑free.”
Theory of Parameters and Reference Frames of
DataA framework that treats
data not as raw, objective facts but as products of specific choices: what to include/exclude, how to
clean and code, what measurement scale to use, and what reference frame (
time, location, demographic) to adopt. Different parameters and frames produce different
data from the same underlying phenomena. This theory opposes
data essentialism – the belief that
data speak for themselves – and insists that understanding
data requires understanding the decisions that shaped them. It is foundational for critical data studies.
Example: “The company claimed their dataset was ‘objective,’ but the theory of parameters and reference frames showed they had excluded non‑English users and filtered out night‑
time activity. The data was real; the frame was engineered.”
