tutorial: make paired_power_tail self-contained (CI codex P3)

Compute the clean-tail boundary as max(cohorts) inside paired_power_tail instead of closing
over the module-level tail_start (= max moderate cohorts). The committed moderate results are
unchanged, but the helper no longer silently uses the wrong tail boundary if a reader reuses
it with fast/slow/custom cohorts.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>

Author: igerber

docs/tutorials/24_staggered_vs_collapsed_power.ipynb

@@ -37,10 +37,10 @@
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@@ -803,10 +803,10 @@
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    "outputs": [
@@ -831,11 +831,11 @@
     "# ...and it's not just unbiased: under FLAT effects with a clean tail, the clean-tail 2x2\n",
     "# also detects a smaller lift than CS (it pools the whole tail into one tight contrast).\n",
     "def paired_power_tail(cohorts, lift, n_sims=250, seed0=4000):\n",
-    "    rs = min(cohorts)\n",
+    "    rs, ts = min(cohorts), max(cohorts)          # clean tail starts after the last wave\n",
     "    rej_tail, rej_cs = [], []\n",
     "    for i in range(n_sims):\n",
     "        p = make_panel(cohorts, lift, seed0 + i, growth=0.0)        # flat effects\n",
-    "        rej_tail.append(fit_2x2(p, rs, tail_start=tail_start)[3])   # clean-tail 2x2\n",
+    "        rej_tail.append(fit_2x2(p, rs, tail_start=ts)[3])           # clean-tail 2x2\n",
     "        rej_cs.append(fit_cs(p)[3])\n",
     "    return np.mean(rej_tail), np.mean(rej_cs)\n",
     "\n",