The charts in this fairness dashboard is designed to help players and observers understand how draws of a lottery game that pre-issued ticket numbers behave over time. The dashboard uses historical draw data from the past draws to show whether the results are evenly distributed across digits, number ranges, and number positions.
Mega Jackpot Charts
Digit Frequency Monitor
The Digit Frequency Monitor shows how often each digit 0-9 appears in the winning numbers of Mega Jackpot. Since each draw produces a 6-digit number, the dashboard counts every digit appearing in all positions upto 1000 draws. The results are displayed as a chart or table showing the percentage frequency of each digit.
Position Bias Checker
The Position Bias Checker examines each digit position in the winning numbers of Mega Jackpot to ensure that every digit (0-9) appears with equal likelihood in each position. Instead of combining all digits together, this method isolates positions to show if any digit appear more often than expected in a specific position. Since each position can contain any digit from 0 to 9, the expected frequency is 10% per digit per position over a large number of draws.
Number Range Distribution
The Number Range Distribution analyzes how winning numbers are spread across the full range of possible values (000000-199999). Instead of looking at individual digits, this method groups numbers into blocks and checks whether winners are evenly distributed across these ranges. In a fair system, each range should receive roughly the same number of winning results over time.
Sample Size
Confidence Meter
To make a reasonable conclusion, 1,000 ticket numbers is enough for the charts above. For 1,000 ticket numbers there are 6000 digits analyzed, 1,000 observations per position, and 1000 total numbers for range distribution. At this scale, the expected value of 10% has a typical variation of ±1% to ±2%. So if a digit shows 9% to 11% is considered normal, 8% to 12% is still acceptable, and outside that consistently is worth investigating.
Large sample sizes are needed because randomness creates misleading patterns in small data. Each chart has a different “effective sample size”. 1,000 draws is a solid baseline for fairness analysis, but not absolute proof. So even with the same number of draws, some charts stabilize faster than others.