Mega Millions Results
On Tuesday night, April 16, 2024, the Mega Millions draw in Massachusetts brought 21 26 36 44 59 back after days away. Given an expected cadence of 1 in 12,103,014 draws, this interval places the result well beyond typical spacing and makes it a meaningful entry for long-term distribution tracking.
Winning numbers for 1 draw on April 16, 2024 in Massachusetts.
Draw times: Evening.
Our take on the Mega Millions results
April 16, 2024Mega Millions report — Tuesday night, April 16, 2024: 21 26 36 44 59 shows a notable pattern
On Tuesday night, April 16, 2024, the Mega Millions draw in Massachusetts brought 21 26 36 44 59 back after days away. Given an expected cadence of 1 in 12,103,014 draws, this interval places the result well beyond typical spacing and makes it a meaningful entry for long-term distribution tracking.
Overview
On Tuesday night, April 16, 2024, the Mega Millions draw in Massachusetts brought 21 26 36 44 59 back after days away. Given an expected cadence of 1 in 12,103,014 draws, this interval places the result well beyond typical spacing and makes it a meaningful entry for long-term distribution tracking.
Combo Profile
From a number profile angle, this draw uses 5 distinct numbers while showing no repeats. The numbers cover 21 to 59 with a wide range.
Why Droughts Matter
Long gaps are context, not a signal - they record variance across time. They offer context for distribution stability over time.
Data Notes
The approach: this report summarizes results recorded for Tuesday night, April 16, 2024 with comparison to long-run frequency baselines. This is documentation, not a forecast.
From Stepzero
The core idea: this reporting is designed to sustain continuity in the archive for analysts and long-run tracking. The priority is accuracy and continuity.
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.
Long-horizon measurement matters most when viewed across extended windows. As samples expand, the distribution becomes clearer and anomalies settle into their expected ranges.
Long-horizon tracking is the only reliable way to separate short-term noise from persistent drift. By logging each outcome against its expected cadence, the system builds a distribution profile that becomes more stable as the sample grows.
Adding to the Long-Term Record
In long-horizon tracking, this entry adds another archive entry to the cumulative record. Long-horizon stability comes from accumulation.