Clarify effect summary description in notebooks

Author: drbenvincent

docs/source/notebooks/geolift1.ipynb

@@ -574,7 +574,7 @@
    "source": [
     "## Effect Summary Reporting\n",
     "\n",
-    "For a decision-ready summary, you can use the `effect_summary()` method which provides a concise report with average and cumulative effects, HDI intervals, tail probabilities, and relative effects. This gives you a CausalImpact-style summary without manual post-processing.\n"
+    "For a decision-ready summary, you can use the `effect_summary()` method which provides a concise report with average and cumulative effects, HDI intervals, tail probabilities, and relative effects. This provides a comprehensive summary without manual post-processing.\n"
    ]
   },
   {

docs/source/notebooks/its_covid.ipynb

@@ -311,7 +311,7 @@
    "source": [
     "## Effect Summary Reporting\n",
     "\n",
-    "For decision-making, you often need a concise summary of the causal effect with key statistics. The `effect_summary()` method provides a decision-ready report with average and cumulative effects, HDI intervals, tail probabilities, and relative effects. This gives you a CausalImpact-style summary without manual post-processing.\n",
+    "For decision-making, you often need a concise summary of the causal effect with key statistics. The `effect_summary()` method provides a decision-ready report with average and cumulative effects, HDI intervals, tail probabilities, and relative effects. This provides a comprehensive summary without manual post-processing.\n",
     "\n",
     ":::{note}\n",
     "Note that in this example, the data has been standardized, so the effect estimates are in standardized units. When interpreting the results, keep in mind that the effects are relative to the standardized scale of the outcome variable.\n",

docs/source/notebooks/its_pymc.ipynb

@@ -1059,7 +1059,7 @@
     "- Tail probabilities (e.g., P(effect > 0))\n",
     "- Relative effects (% change vs counterfactual)\n",
     "\n",
-    "This gives you a CausalImpact-style summary without manual post-processing.\n"
+    "This provides a comprehensive summary without manual post-processing.\n"
    ]
   },
   {

docs/source/notebooks/sc_pymc_brexit.ipynb

@@ -659,7 +659,7 @@
    "source": [
     "## Effect Summary Reporting\n",
     "\n",
-    "For decision-making, you often need a concise summary of the causal effect with key statistics. The `effect_summary()` method provides a decision-ready report with average and cumulative effects, HDI intervals, tail probabilities, and relative effects. This gives you a CausalImpact-style summary without manual post-processing.\n"
+    "For decision-making, you often need a concise summary of the causal effect with key statistics. The `effect_summary()` method provides a decision-ready report with average and cumulative effects, HDI intervals, tail probabilities, and relative effects. This provides a comprehensive summary without manual post-processing.\n"
    ]
   },
   {