Missing Data Imputation

Real-world data goes missing all the time — a sensor drops offline, a form field gets skipped, a survey section never reaches part of the sample, a record gets accidentally deleted. The ideal fix is often to collect more data, but that's not always possible: if a weather station's thermometer goes offline for an afternoon, there's no going back to re-measure that afternoon's temperature after the fact. Imputation is one way to handle a gap like that — instead of discarding the good data around it or leaving a hole, you fill it in with a plausible estimate based on the pattern the rest of the data shows. This chart demonstrates that with a scatter of tea steep time (minutes) vs. strength (1-10) — the true relationship is roughly linear, then curves as strength approaches its maximum. Choose a window of time where data goes missing, then click through different ways of filling in the gap.

Drag either edge of the shaded band directly (or use the two sliders below) to choose which time window is missing — the band only appears once you pick a method other than "Original data", since nothing's missing there yet — then switch between methods to see how each one fills in the blanks, and compare the filled-in (diamond) points to the true values (hollow circles) they're standing in for.

What this teaches: Remove a window of points from a time series, fill the gap with different imputation methods, and compare each fill with the values that were really there. It teaches how the choice of imputation method can distort a trend or understate variability.