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Abstract:We present a sequential testing method to identify a practically significant effect. We build on the existing mixture sequential probability ratio test (mSPRT) that can sequentially test for a non-zero treatment effect by using a truncated mixing distribution to differentiate between effects that are large enough to merit a real world action versus that are non-zero but too small to merit a real world action. We verify the Type-I error control of our method theoretically and empirically. We also extend this idea to sequentially test for one-sided practical significance such as non-inferiority testing, and show that we may still effectively control the Type-I error rate.
From: Kyu Min Shim [view email]
[v1]
Tue, 9 Sep 2025 16:22:10 UTC (10,788 KB)
[v2]
Tue, 23 Jun 2026 17:52:00 UTC (1 KB) (withdrawn)
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