Cash5 Results
On Monday night, May 11, 2026, for Connecticut's Cash5 draw, 03 18 20 22 32 returned after a -day gap in Connecticut. By the expected cadence of 1 in 324,632 draws, the interval is a long-gap event.
Winning numbers for 1 draw on May 11, 2026 in Connecticut.
Draw times: Evening.
Our take on the Cash5 results
May 11, 2026Cash5 report — Monday night, May 11, 2026: 03 18 20 22 32 shows a notable pattern
On Monday night, May 11, 2026, for Connecticut's Cash5 draw, 03 18 20 22 32 returned after a -day gap in Connecticut. By the expected cadence of 1 in 324,632 draws, the interval is a long-gap event.
Overview
On Monday night, May 11, 2026, for Connecticut's Cash5 draw, 03 18 20 22 32 returned after a -day gap in Connecticut. By the expected cadence of 1 in 324,632 draws, the interval is a long-gap event.
Combo Profile
As a number pattern, 03 18 20 22 32 uses 5 distinct numbers and a wide spread from 3 to 32.
Why Droughts Matter
A long drought is descriptive rather than predictive. It records variance across time and helps analysts evaluate whether outcomes are tracking within expected frequency bands or drifting into the tails of the distribution.
Data Notes
To clarify: this report captures results recorded for Monday night, May 11, 2026 with reference to historical frequency baselines. The goal is context, not prediction.
From Stepzero
Stepzero produces these reports to provide a calm, evidence-first record of how draw patterns unfold over time. The aim is clarity and continuity - a reference point for long-horizon tracking rather than a call to action.
Additional Context
Long-horizon measurement matters most when viewed across extended windows. As samples expand, the distribution becomes clearer and anomalies settle into their expected ranges. Record-keeping at scale becomes the foundation for analysis. Each outcome, whether typical or unusual, contributes to the stability and clarity of the long-run picture.
Adding to the Long-Term Record
With its return, 03 18 20 22 32 contributes another meaningful data point to the historical dataset. Each draw - whether routine or statistically unusual - refines the long-term view of how large random systems behave over time.