Data Triangulation: Combining Independent Estimates (2D)

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 as a beeswarm on a shared, pannable and zoomable value axis, 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.