diff --git a/services/experimentAnalysis.ts b/services/experimentAnalysis.ts
index 774c575..cc24e2a 100644
--- a/services/experimentAnalysis.ts
+++ b/services/experimentAnalysis.ts
@@ -40,7 +40,6 @@ interface ComputedWeeklyMetrics extends WeeklyMetrics {
   cvr: number;
   ctr: number;
   roas: number;
-  fraction?: number;
 }
 
 function getISOWeekStart(year: number, week: number): number {
@@ -126,67 +125,32 @@ function splitPeriods(
   startTs: number,
   endTs: number,
   beforeStartTs: number,
-  beforeEndTs: number // Added to support separated custom periods
+  beforeEndTs: number
 ): { before: ComputedWeeklyMetrics[]; after: ComputedWeeklyMetrics[] } {
   const before: ComputedWeeklyMetrics[] = [];
   const after: ComputedWeeklyMetrics[] = [];
 
   for (const w of data) {
     const weekStartTs = w.timestamp;
-    const weekEndTs = w.timestamp + 6 * 86400000 + 86399999; // End of the 7th day
+    const weekEndTs = w.timestamp + 6 * 86400000 + 86399999;
 
-    // A week overlaps the treatment period if it starts before the period ends, 
-    // AND ends after the period starts.
     const overlapsAfter = weekEndTs >= startTs && weekStartTs <= endTs;
     const overlapsBefore = weekEndTs >= beforeStartTs && weekStartTs < beforeEndTs;
 
-    // We prioritize assigning to 'after' so that we capture all units happening 
-    // closely around the experiment dates for accurate reporting.
     if (overlapsAfter) {
-      const overlapStart = Math.max(weekStartTs, startTs);
-      const overlapEnd = Math.min(weekEndTs, endTs);
-      const overlapFraction = Math.max(0, Math.min(1, (overlapEnd - overlapStart) / (7 * 86400000)));
-
-      if (overlapFraction > 0) {
-        after.push({
-          ...w,
-          units: w.units * overlapFraction,
-          revenue: w.revenue * overlapFraction,
-          sessions: w.sessions * overlapFraction,
-          cost: w.cost * overlapFraction,
-          clicks: w.clicks * overlapFraction,
-          impressions: w.impressions * overlapFraction,
-          fraction: overlapFraction
-        });
-      }
+      after.push(w);
     } else if (overlapsBefore) {
-      const overlapStart = Math.max(weekStartTs, beforeStartTs);
-      const overlapEnd = Math.min(weekEndTs, beforeEndTs);
-      const overlapFraction = Math.max(0, Math.min(1, (overlapEnd - overlapStart) / (7 * 86400000)));
-
-      if (overlapFraction > 0) {
-        before.push({
-          ...w,
-          units: w.units * overlapFraction,
-          revenue: w.revenue * overlapFraction,
-          sessions: w.sessions * overlapFraction,
-          cost: w.cost * overlapFraction,
-          clicks: w.clicks * overlapFraction,
-          impressions: w.impressions * overlapFraction,
-          fraction: overlapFraction
-        });
-      }
+      before.push(w);
     }
   }
 
   return { before, after };
 }
 
-function avgMetric(data: ComputedWeeklyMetrics[], metric: string): number {
-  if (data.length === 0) return 0;
+function avgMetric(data: ComputedWeeklyMetrics[], metric: string, durationWeeks: number): number {
+  if (data.length === 0 || durationWeeks <= 0) return 0;
   const sum = data.reduce((s, w) => s + getMetricValue(w, metric), 0);
-  const totalFraction = data.reduce((s, w) => s + (w.fraction || 1), 0);
-  return totalFraction > 0 ? sum / totalFraction : 0;
+  return sum / durationWeeks;
 }
 
 const METRICS = ['units', 'sessions', 'cvr', 'ctr', 'roas', 'revenue', 'acos'];
@@ -198,12 +162,14 @@ function computeMetricDiD(
   controlBefore: ComputedWeeklyMetrics[],
   controlAfter: ComputedWeeklyMetrics[],
   metric: string,
-  hasControlGroup: boolean
+  hasControlGroup: boolean,
+  treatmentDurationWeeks: number,
+  baselineDurationWeeks: number
 ): DiDMetricResult {
-  const tBefore = avgMetric(treatmentBefore, metric);
-  const tAfter = avgMetric(treatmentAfter, metric);
-  const cBefore = hasControlGroup ? avgMetric(controlBefore, metric) : 0;
-  const cAfter = hasControlGroup ? avgMetric(controlAfter, metric) : 0;
+  const tBefore = avgMetric(treatmentBefore, metric, baselineDurationWeeks);
+  const tAfter = avgMetric(treatmentAfter, metric, treatmentDurationWeeks);
+  const cBefore = hasControlGroup ? avgMetric(controlBefore, metric, baselineDurationWeeks) : 0;
+  const cAfter = hasControlGroup ? avgMetric(controlAfter, metric, treatmentDurationWeeks) : 0;
 
   let didEstimate: number;
   if (hasControlGroup) {
@@ -296,12 +262,16 @@ export function computeDiD(
     ? splitPeriods(controlWeekly, startTs, endTs, beforeStartTs, beforeEndTs)
     : { before: [] as ComputedWeeklyMetrics[], after: [] as ComputedWeeklyMetrics[] };
 
+  const treatmentDurationWeeks = Math.max(1, Math.round((endTs - startTs) / (7 * 86400000)));
+  const baselineDurationWeeks = Math.max(1, Math.round((beforeEndTs - beforeStartTs) / (7 * 86400000)));
+
   const metrics: Record<string, DiDMetricResult> = {};
   for (const metric of METRICS) {
     metrics[metric] = computeMetricDiD(
       tSplit.before, tSplit.after,
       cSplit.before, cSplit.after,
-      metric, hasControlGroup
+      metric, hasControlGroup,
+      treatmentDurationWeeks, baselineDurationWeeks
     );
   }
 
@@ -366,7 +336,8 @@ export function buildCounterfactualSeries(
 
   if (!hasControlGroup) {
     const { before: beforeData } = splitPeriods(treatmentWeekly, startTs, endTs, beforeStartTs, beforeEndTs);
-    const preAvg = avgMetric(beforeData, metric);
+    const baselineDurationWeeks = Math.max(1, Math.round((beforeEndTs - beforeStartTs) / (7 * 86400000)));
+    const preAvg = avgMetric(beforeData, metric, baselineDurationWeeks);
 
     return treatmentWeekly.map(w => ({
       week: w.week,
@@ -376,14 +347,18 @@ export function buildCounterfactualSeries(
     }));
   }
 
+  // Calculate variances for Bayesian update
+  const baselineDurationWeeks = Math.max(1, Math.round((beforeEndTs - beforeStartTs) / (7 * 86400000)));
+  const treatmentDurationWeeks = Math.max(1, Math.round((endTs - startTs) / (7 * 86400000)));
+
   const controlAsinSet = getExperimentAsins(experiment.control_asins, salesData);
   const controlWeekly = aggregateWeeklyMetrics(controlAsinSet, salesData, experiment.marketplace);
 
   const { before: tBeforeData } = splitPeriods(treatmentWeekly, startTs, endTs, beforeStartTs, beforeEndTs);
   const { before: cBeforeData } = splitPeriods(controlWeekly, startTs, endTs, beforeStartTs, beforeEndTs);
 
-  const tPreAvg = avgMetric(tBeforeData, metric);
-  const cPreAvg = avgMetric(cBeforeData, metric);
+  const tPreAvg = avgMetric(tBeforeData, metric, baselineDurationWeeks);
+  const cPreAvg = avgMetric(cBeforeData, metric, baselineDurationWeeks);
 
   // Build a map of control weekly values
   const controlMap = new Map<string, number>();