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 Data
A 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.”