Can You Trust the Estimate?
Triangulation check
The simulator estimates what happens when one or more factors change. This page re-estimates the same scenario through independent routes and shows them together. Frederick Mosteller called this triangulation: estimate one quantity from several structurally different models, and treat agreement across them as evidence in itself. The propensity route is a model built live from the same predictors but relying on the outcome much less directly. The network route reads the estimate off a fitted Bayesian network under the same reporting rule the simulator uses, so it is the same quantity by a third structure. That independence is the point. When methods resting on different assumptions land close, the result is less likely to be an artifact of one modeling choice. When they diverge, read the estimate cautiously; the cause may be a small sample, weak overlap, residual imbalance, or model dependence. The network route appears only when the fitted graph gives at least one scenario factor a direct path to the outcome.
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Modeling on this page is restricted to binary (yes/no) outcomes.
Auto-Build Actionable found no movable predictors for this outcome, so this simulator uses the best Standard model instead — its predictors may not be classified as directly movable.
Set the scenario
The number on each card is the variable's incremental contribution — the gain in Tjur R² it adds once the other variables are included, the same quantity reported elsewhere on this site. Bars and ordering use it too, largest first. Hover a number to see how much of the variable's one-at-a-time separation that represents.
Predicted outcome
How did you change the shot mix?
Each pair of bars compares the actual distribution (green) to the distribution under your scenario (red where you changed it). This is what you changed — not the predicted impact, just the input.
How certain is this result?
Every prediction has wiggle room — these histograms show how much. The green bars are the plausible answers for the baseline; the red bars are the plausible answers for your scenario. Where the colors overlap, the two answers are close enough that the model can't cleanly tell them apart.
Set-everyone-to-X table
Click to show predicted outcomes for every level of every variable
Set-everyone-to-X table
Click to show predicted outcomes for every level of every variable
For each variable's level, the predicted outcome if every respondent had that response, all else unchanged. This is the analytic view of the simulator.
Support and calibration
This panel answers two questions. First, is there enough survey data behind your scenario? It shows how many people actually gave the answers you picked, and warns you if any of those answers was given by too few people to trust. Second, how well does the model match the survey?
Explore each factor
For each variable, see how the predicted outcome would change if you switched just that one selection — holding all your other selections fixed. This shows which individual factor settings produce the largest model-implied changes in the current scenario.
Run a scenario above and we’ll cross-check that exact change here — the simulator’s number beside a structurally different propensity estimate.