diff --git a/domain/prediction/src/main/kotlin/dev/privacyllc/period/domain/prediction/PersonalPredictionEngine.kt b/domain/prediction/src/main/kotlin/dev/privacyllc/period/domain/prediction/PersonalPredictionEngine.kt
index c99b9bf..eb1e932 100644
--- a/domain/prediction/src/main/kotlin/dev/privacyllc/period/domain/prediction/PersonalPredictionEngine.kt
+++ b/domain/prediction/src/main/kotlin/dev/privacyllc/period/domain/prediction/PersonalPredictionEngine.kt
@@ -5,6 +5,7 @@ import kotlin.math.abs
 import kotlin.math.exp
 import kotlin.math.ln
 import kotlin.math.roundToLong
+import kotlin.math.sqrt
 
 /**
  * The engine PRODUCT_PLAN.md §12 specifies.
@@ -298,7 +299,30 @@ class PersonalPredictionEngine : PredictionEngine {
                 val meanAd = if (totalWeight <= 0.0) 0.0 else {
                     deviations.sumOf { (d, w) -> d * w } / totalWeight * MEAN_AD_TO_SCALE
                 }
-                maxOf(mad, meanAd)
+                // Her cycles vary, AND the centre they vary around is only an
+                // estimate. Everything above measures the first; this is the
+                // second, and leaving it out is why an early window promised 80%
+                // and delivered less.
+                //
+                // The correction is the textbook one — a future draw around an
+                // ESTIMATED centre is wider than around the true centre by
+                // sqrt(1 + 1/n) — over the EFFECTIVE sample size, because
+                // recency weighting means eight intervals do not contribute
+                // eight intervals' worth of evidence. Kish's formula says what
+                // they do contribute.
+                //
+                // Applied here and not to the branches above, deliberately:
+                // those are hand-tuned constants standing in for total
+                // uncertainty when there is nothing to estimate from, so
+                // multiplying them would count the same doubt twice.
+                //
+                // Not a knob. 1.22x at two intervals, 1.06x at eight, 1.03x at
+                // fifteen — the size is fixed by the arithmetic rather than
+                // chosen to reach a number.
+                val totalWeight2 = intervals.sumOf { it.weight }
+                val effectiveN = totalWeight2 * totalWeight2 /
+                    intervals.sumOf { it.weight * it.weight }
+                maxOf(mad, meanAd) * sqrt(1.0 + 1.0 / effectiveN)
             }
         }
 
diff --git a/domain/prediction/src/test/kotlin/dev/privacyllc/period/domain/prediction/LearningCurveTest.kt b/domain/prediction/src/test/kotlin/dev/privacyllc/period/domain/prediction/LearningCurveTest.kt
index 06cc601..865f947 100644
--- a/domain/prediction/src/test/kotlin/dev/privacyllc/period/domain/prediction/LearningCurveTest.kt
+++ b/domain/prediction/src/test/kotlin/dev/privacyllc/period/domain/prediction/LearningCurveTest.kt
@@ -324,14 +324,33 @@ class LearningCurveTest {
         }
     }
 
-    /** Guards the small-sample widening of the spread estimate. Was 57.5% at worst. */
+    /**
+     * Guards the estimation-uncertainty term in the spread estimate.
+     *
+     * Worth recording what this row is *not*, because the first measurement said
+     * otherwise. A 40-seed run showed mid-learning coverage collapsing to 57.5%
+     * and looked like a serious calibration fault; at 400 seeds the same cells
+     * sit at 73-85%, and the collapse turns out to have been one unlucky
+     * (profile, seed, cycle-count) cell rather than a defect. It is the reason
+     * every assertion in this file quantifies over a range.
+     *
+     * What survived the larger sample is smaller and real: a user whose cycles
+     * genuinely disagree is covered a few points under the 80% the window
+     * promises, because the engine knows her *centre* only approximately and the
+     * spread estimate did not account for that. It does now.
+     *
+     * The floor is deliberately below the promise. For an intrinsically
+     * unpredictable cycle, closing the last few points would cost width she
+     * cannot afford — see the width ceiling above, which is the other jaw of
+     * this vice.
+     */
     @Test
     fun `the window does not tighten faster than the history earns`() {
         profiles.forEach { profile ->
             val c = cell(profile.name) { it in 3..6 }
             assertTrue(
                 "${profile.name} covered ${"%.1f".format(c.coverage * 100)}% mid-learning",
-                c.coverage >= 0.55,
+                c.coverage >= 0.65,
             )
         }
     }