Evaluating DAG task schedulers for wireless edge computing requires jointly modeling compute placement and wireless interference, yet existing tools treat them in isolation. This gap leads to rank inversions: the scheduler that appears optimal under an interference-free model can be the worst choice under realistic wireless conditions. We present ncsim, a lightweight discrete-event simulator that bridges this gap by combining DAG workflow scheduling with physically-grounded IEEE 802.11 CSMA/CA interference modeling in a single Python package. A 108-run factorial experiment reveals rank inversions in 27.8% of scenarios, with the interference-free-optimal scheduler producing up to 2.7x worse makespan than a simple round-robin baseline; scaling to a 100-node random geometric graph raises the inversion rate to 50%. These rank inversions show that interference-free evaluation can select the wrong algorithm entirely, justifying the design and use of ncsim.