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Data Triangulation: Combining Independent Estimates

Interactive 3D research-methodology simulator: three independent methods (survey, experiment, observation) each estimate an unknown quantity with their own bias and noise, and inverse-variance weighting triangulates them into one combined estimate — watch precision improve but see that no amount of triangulation removes a biased source's pull.

Statistics3DModerate60 FPS📱 Mobile-adapted⇄ 2D version

Three independent research methods — a survey, a controlled experiment and a field observation — each try to estimate the same unknown quantity, each with its own bias and noise. This simulator draws real Gaussian sample clouds for every method in 3D, computes each one's standard error from its noise and sample size, and fuses them with inverse-variance weighting into a single triangulated estimate. Tune each method's precision and the observational method's systematic bias to see the central lesson of triangulation: pooling reduces random error fast, but no amount of combining removes a biased source's pull on the truth.

⚙ Under the hood

Three independent research methods — a survey, a controlled experiment and a field observation — estimate the same unknown quantity with their own bias and noise, and inverse-variance weighting fuses them into one triangulated estimate whose precision, but not its bias, improves with sample size.

research methodstriangulationstatisticssamplingmeta-analysisbias vs variance

3D · Three.js / WebGL renderer · 60 FPS target · runs fully client-side, no install