Mean and median describe different things
The arithmetic mean sums values and divides by their count. The median is the middle of the ordered sample; with an even number of values, it averages the two central values.
Mean: 3.00×
The high result pulls the average upward.
Median: 1.30×
The middle observation stays near the lower values.
Neither statistic determines the next bust. Confirm whether a displayed metric is an ordinary statistic or a proprietary indicator before attaching a definition to its label.
Normalisation needs a stated reference
Normalisation expresses values relative to a chosen scale or reference. For a simple public example, a value of 3 compared with a reference of 2 has a ratio of 1.5. That ratio alone does not establish an advantage or a probability.
This example does not describe Casino Probe’s private normalised metrics. For those, this library explains their visible readings and role in the workspace without publishing their computation.
Read the window with the value
The Medians & Trends and Indi views show windows of 50, 100, 200, 500 and 1,000 results. A short window can react differently from a long one. Overlapping windows share observations, so matching readings are not automatically independent confirmations.
Absences put a gap in context
The Absence Table presents Number, Current, Record and % Rec. Read the current gap alongside its recorded reference, and confirm what the row’s number represents. A historical record can be exceeded.
An illustrative gap of 8 compared with a record of 32 is one quarter of that record. This comparison of counts is not a 25% chance of a future appearance and does not create a countdown to a guaranteed result.
Patterns, Force and other views
Pattern and sequence views organise recorded structures. Force and the other indicators offer additional readings to compare with recent history. Their labels and colours should be interpreted within their intended view, not treated as universal probabilities.
Casino Probe’s proprietary calculations remain private. This chapter gives a functional reading guide rather than reproducing the processing implementation. Explore the widgets in How it Works, then use the analysis workflow to organise your observations.