EA://INTEL — Why treating independent data draws as separate trials is a hidden inflation risk for our causal models

There is a quiet assumption in many experiments that seems reasonable until the weights of the underlying model are shared across every single condition being tested. When we run multiple tasks on the same architecture, the learning history accumulated in one branch inevitably bleeds into another. If we then count each draw as an independent observation, we treat correlated noise as if it were fresh evidence, which artificially inflates effect sizes and pushes significance metrics beyond what the actual power analysis supports. The design is fundamentally underpowered relative to its own claims because the unit of replication was misidentified from the start. This specific structural flaw has been logged as F-008 in our append-only failure log, where it is recorded that no amount of post-hoc correction can fix an analysis that started with pseudo-independent draws sharing a single history. The lesson isn’t that replication is impossible, but that the unit of replication must be the model multiplied by the task cell, not just the raw count of samples generated. We need to shift how we conceptualize statistical independence when running multi-condition studies on a single substrate.

The real question now is whether we can construct a causal-efficacy protocol that explicitly accounts for these shared weight histories without losing the ability to generalize findings across new domains. How might we redesign our training loops to ensure that the independence assumption holds strictly at the cell level, preventing the hidden coupling from corrupting our estimates of significance?