Do the contents of an entry affect the draw?
No. Names, locations, and other entry details have no influence on selection. The random system is fully separated from entry content and focuses only on the number of entries, not their specific details.
Fairness and randomness
Every valid entry has an equal chance of winning. No exceptions.
Every draw on randomdraws.co.nz™ uses a random process designed to give each entry the same mathematical chance, from the first row to the last. This approach has been independently tested through millions of simulated draws, so promoters can run draws with confidence and entrants can trust every result.
Each draw is accompanied by a Certificate of Compliance, and can be verified through our draw lookup. Our platform is government certified and compliant across multiple jurisdictions.
How draws work
Every draw starts with randomness generated from physical processes inside server hardware, not from predictable software patterns. This gives each selection a genuinely unpredictable starting point that no one can forecast or influence.
That raw randomness is then processed through a selection method that guarantees every valid entry has a mathematically identical probability of being chosen. No entry can have even a fractional advantage over another.
The entire system has been verified through millions of simulated draws at a standard that exceeds the confidence levels used by lottery regulators and gaming commissions. The results confirm that real outcomes match a truly fair model.
Common questions
Does the order of entries in the file matter?
No. Entry position has zero effect on the outcome. The random selection process treats every entry identically, whether it appears at the top, middle, or bottom of the file.
Do the results of previous draws affect future draws?
No. Every draw is independent. Previous outcomes have zero influence on future results, even if some entrants appear in multiple draws. Each run starts fresh and applies the same fairness rules again. For example, if an entrant wins one draw, that does not increase or decrease their chances in any future draw they enter.
Do more entries mean more chance of winning?
Yes, if the draw is configured that way. Ticketed draws are an optional feature that allow promoters to assign a ticket or entry count to each entrant, giving them a proportionally higher chance of winning. Each ticket is treated as a separate entry in the random selection process, so more tickets mean more chances. If a standard, non-ticketed draw is used, then every row in the file counts as one entry, and every entry has the same chance to win.
Can anyone, including the promoter, influence the results?
The selection process is automated and built around unpredictable randomness. If complete and accurate entries are provided, no one can influence the outcome, including the promoter, randomdraws.co.nz™, or any third party.
The organising entity (e.g. promoter) is responsible for providing complete and accurate entries for use in our system. Prize fulfilment and winner communication also remain the responsibility of the organising entity. We provide promoters with the option to enable entry checking and winner publication for additional transparency.
Someone with very few entries won. Is that possible?
Yes, and it is expected to happen regularly. The key is to distinguish between two different questions:
- What are the odds that a specific person with few entries wins? Low, exactly proportional to their share of entries.
- What are the odds that any person with few entries wins? Often surprisingly high, because there are usually many such people.
For example, if 900 out of 1,000 entrants each hold a single ticket and the remaining 100 hold ten tickets each, the chance that some single-ticket holder wins is roughly 47%. It feels unlikely only because we focus on the winner after the fact, not on the large pool they came from. This is a well-documented cognitive bias known as the Prosecutor's Fallacy: confusing the low probability of a specific outcome with the much higher probability that some similar outcome occurs.
What if the same person wins more than one draw?
Every draw is independent, so winning one draw has no effect on the next. A repeat winner is a natural consequence of running many draws with overlapping entrant pools.
The distinction that matters is between two different probabilities:
- A specific entrant winning twice is unlikely for any single individual.
- Any entrant in multiple draws winning twice is much more likely, and the odds of it occurring increase with every draw that runs with the same entrants.
This is the same principle behind the Birthday Paradox: in a room of just 23 people, there is a greater than 50% chance that two share a birthday. The number seems impossibly low until you consider that the comparison is not between one specific pair, but across every possible pair in the room. Repeat winners follow exactly the same mathematics.
In ticketed draws, a repeat winner may also have held a proportionally larger share of entries in each draw, making each individual win entirely consistent with the expected odds.
Randomness and human intuition
Humans are remarkably good at finding patterns, even where none exist. This served us well in nature but it works against us when evaluating random outcomes. The following are well-documented cognitive biases that commonly arise when people assess draw results. Each one has decades of published research behind it.
The Birthday Paradox
In a group of just 23 people, there is a greater than 50% chance that two share a birthday, despite there being 365 possible dates. The number feels far too low because we instinctively think about one specific pair rather than every possible pair. The same effect explains why repeat winners appear across the draws of a promoter with overlapping entrant pools: the chance that someone wins twice is far higher than the chance that a particular person does.
The Prosecutor's Fallacy
After a surprising outcome, it is natural to calculate how unlikely that specific result was and conclude something must be wrong. But this reasoning is flawed. In any draw with many entrants, every possible winner was individually unlikely. The correct question is not "what were the odds of this person winning?" but "what were the odds of some outcome that would have surprised us?" That second probability is almost always much higher.
The Gambler's Fallacy
The belief that past outcomes influence future independent events. For example, if someone did not win the last three draws, they are not "due" for a win. Each draw is fully independent. The random selection process has no memory of any previous outcomes, so the odds remain the same regardless of past results.
The Clustering Illusion
Truly random data often contains clusters that look like patterns. For example, winners might come from the same region several times in a row, or entries near the top of a file might seem to win more often over a short period. These streaks are a normal property of randomness, not evidence of bias. Over larger samples, the distribution evens out.
Confirmation Bias
People notice and remember outcomes that match their suspicions and overlook the many outcomes that do not. One unusual result can feel like a pattern, while thousands of ordinary results go unnoticed. Fair evaluation requires looking at the full set of outcomes, not just the ones that caught our attention.
Under the hood
For technically curious readers, this is how the platform enforces fairness at a deeper level.
Hardware entropy
Randomness is generated from physical processes inside server hardware rather than predictable software patterns. That gives the draw engine an unpredictable starting point for every selection. The result is random input that cannot be forecast or pre-planned.
Rejection sampling
Raw random values are filtered through a selection method that preserves equal probability for every entry. This prevents tiny numerical imbalances from favouring one entry over another. Every valid entry keeps a mathematically identical chance of being chosen.
Mathematical verification techniques
The system has been verified through millions of simulated draws at a standard that exceeds the confidence levels used by lottery regulators and gaming commissions. It confirms that real outcomes match the fairness model over large sample sizes. Some of the mathematical techniques used include:
- Chi-squared tests to compare observed outcomes against expected distributions
- Kolmogorov-Smirnov tests to assess the randomness of selection patterns
- Monte Carlo simulations to model millions of draws and confirm alignment with theoretical probabilities
- Entropy analysis to ensure the randomness source provides sufficient unpredictability
- Rejection sampling validation to confirm that the selection method maintains equal probabilities for all entries