Count the relevant units
A study of completed withdrawals can count transactions, customers or accounts. Ten withdrawals from one account are not necessarily ten independent customer experiences. A slot study can count paid rounds or individual stages within features. Mixing these units makes averages and uncertainty measures difficult to interpret.
Suppose 20 of 100 observed rounds pay any prize. The observed hit proportion is 20%. Another sample of ten rounds with three hits produces 30%, but its smaller denominator makes it more sensitive to each outcome. Neither count alone establishes the full theoretical payout distribution or RTP.
Rare events require special care
A large jackpot can contribute meaningfully to theoretical return while appearing rarely. A sample containing no jackpot may estimate return poorly even when it includes many ordinary rounds. The required size depends on the outcome distribution and intended precision; there is no universal round count that verifies every game.
Quality and representativeness
Record the configuration, time period, currency, eligibility and selection method. A collection containing only successful withdrawals cannot estimate the share of all requests that succeeded. Excluding incomplete cases without explanation can bias a timing claim. Larger biased samples can make an incorrect conclusion look more precise.
Reporting a result usefully
State the count, unit, method, exclusions and uncertainty alongside the average or percentage. Separate personal observations from population claims. A sample-size label should help readers understand evidence scope, not serve as a decorative authority badge. One completed test can establish that event occurred, but it cannot by itself establish a typical experience for everyone.
Does collecting more screenshots improve a study automatically?
Not if they repeat the same event or use inconsistent selection. Ten captures of one withdrawal are still one transaction observation. Screenshots collected only when a payment is fast omit slower or unresolved cases and can bias a claimed typical time. Define the observation unit before counting evidence files. A useful dataset records all eligible cases under the chosen method, their stages and exclusions. File count, transaction count and customer count should appear as distinct measures when they answer different questions.