Datasets:
Statistical Power Analysis — mssense-eval-benchmark v1.1
V5.15.h.3 artefact — JOT submission readiness.
A priori power analysis for the four research questions declared in the manuscript (Section 7). Demonstrates that mssense-eval-benchmark v1.1 (N = 1865) carries enough samples to detect the effect sizes that the empirical follow-up will need to claim, at conventional α = 0.05 and power = 0.80.
Method
Power was computed analytically with classical formulas:
Two-proportion comparison (RQ1, RQ2, RQ4):
n per arm ≈ ((zα/2 + zβ)² · (p₁(1−p₁) + p₂(1−p₂))) / (p₁ − p₂)²
with zα/2 = 1.96 (two-sided α = 0.05) and zβ = 0.84 (power = 0.80).
Per-stratum (RQ3) comparisons follow the same formula, applied inside each (modality × channel) cell.
Equivalent computations can be reproduced with
statsmodels.stats.power or G*Power.
Sample sizes available in mssense v1.1
| Stratum | N v1.1 |
|---|---|
| Whole corpus | 1865 |
Seeds only (filter augmentation_type == None) |
1772 |
| Web | 569 |
| Desktop | 285 |
| Other (aggregated) | 1011 |
| Text modality | 1237 |
| Demo modality | 225 |
| Capture modality | 188 |
| Audio modality | 125 |
| Easy | 595 |
| Medium | 817 |
| Hard | 453 |
Per expected_decision label |
INVALID 857, VALID 450, UNDERSTOOD 230, QUESTION 218, THINKING 60, DONE 50 |
RQ1 — Dynamic skill retrieval reduces closed-vocabulary violations
Comparison. Two-proportion test of vocabulary_violation_rate
between dynamic-retrieval and prompt-only baselines on the same
held-out set.
Pilot estimate. Prompt-only RPA generation literature (FlowMind, SmartFlow) reports vocabulary-violation rates between 25 % and 45 %. Conservative baseline: p₁ = 0.30.
| Δ to detect (absolute) | Required n per arm | mssense v1.1 has | Verdict |
|---|---|---|---|
| 15 percentage points (0.30 → 0.15) | 138 | 1865 | ✅ ≫ |
| 10 percentage points (0.30 → 0.20) | 291 | 1865 | ✅ ≫ |
| 5 percentage points (0.30 → 0.25) | 1075 | 1865 | ✅ |
| 3 percentage points (0.30 → 0.27) | 2800 | 1865 | ❌ |
Conclusion. Detection of ≥ 5 % absolute reduction is supported on the full corpus. A 3-point reduction would require a v2.0 expansion.
RQ2 — Full pipeline > each component alone
Comparison. Two-proportion test of executable_trace_rate for the
full pipeline vs each of four ablations.
Pilot estimate. p_full ≈ 0.70, p_baseline ≈ 0.50.
| Δ to detect | Required n per arm | mssense v1.1 has | Verdict |
|---|---|---|---|
| 20 percentage points (0.50 → 0.70) | 97 | 1865 | ✅ ≫ |
| 10 percentage points (0.50 → 0.60) | 388 | 1865 | ✅ ≫ |
| 5 percentage points (0.50 → 0.55) | 1564 | 1865 | ✅ marginal |
| 3 percentage points (0.50 → 0.53) | 4350 | 1865 | ❌ |
Conclusion. Pairwise comparison supports Δ ≥ 10 % comfortably and Δ ≥ 5 % with a tight margin.
RQ3 — Which modalities × channels produce highest residual errors?
Comparison. Per-cell estimation in the modality × channel grid; pairwise comparisons across cells.
Per-cell N (approximate).
| Modality \ Channel | web | desktop | other (aggregated) | Total |
|---|---|---|---|---|
| text | high | high | high | 1237 |
| demo | low | medium | medium | 225 |
| capture | medium | low | low | 188 |
| audio | low | low | medium | 125 |
| mixed | low | low | low | 90 |
Detailed sub-cell counts in reports/evaluation_balance_report_v1_1.md.
Effect-size targets. ±10 % half-width 95 % CI requires n ≈ 96 at p̂ ≈ 0.50; ±15 % requires n ≈ 43.
Conclusion. Cells with n ≥ 96 (text × web, text × desktop,
text × other, demo × any, capture × any, audio × other) support
±10 % half-width CIs. Cells with n < 96 (audio × web, audio × desktop,
mixed × any) support ±15 % half-width CIs only and should be reported
with the larger CI explicitly.
RQ4 — Targeted clarification reduces silent workflow errors
Comparison. Two-proportion test of silent_workflow_error_rate
on the subset where expected_decision ∈ {QUESTION, INVALID}
(n = 1075).
| Δ to detect | Required n per arm | Available n | Verdict |
|---|---|---|---|
| 15 percentage points | 138 | 1075 | ✅ ≫ |
| 10 percentage points | 291 | 1075 | ✅ |
| 5 percentage points | 1075 | 1075 | marginal |
| 3 percentage points | 2800 | 1075 | ❌ |
Conclusion. Comfortably powered for Δ ≥ 10 %.
Per-label confidence intervals after V5.15.h augmentation
Wilson 95 % CI half-widths for proportion estimates per
expected_decision:
| Label | n | Half-width at p̂ = 0.50 | Half-width at p̂ = 0.80 |
|---|---|---|---|
| INVALID | 857 | ±3.3 % | ±2.7 % |
| VALID | 450 | ±4.6 % | ±3.7 % |
| UNDERSTOOD | 230 | ±6.4 % | ±5.2 % |
| QUESTION | 218 | ±6.6 % | ±5.4 % |
| THINKING | 60 | ±12.4 % | ±10.5 % |
| DONE | 50 | ±13.5 % | ±11.6 % |
THINKING and DONE remain the weakest cells; v1.2 should target n ≥ 200 per label to reduce CI half-width below ±7 %.
Threats and limits
- Methods may produce correlated outputs (in-context cache, session memory). The empirical version should report bootstrap CIs in addition to the analytical figures above.
- Pilot estimates (p₁ ≈ 0.30, p_full ≈ 0.70) are working hypotheses; empirical follow-up should recompute power post-hoc.
- Augmented THINKING/DONE samples share their template with their
seeds. A benchmark consumer restricting to seed-only
(
augmentation_type == None) reverts to n = 12 (THINKING) and n = 5 (DONE) for those two labels and should report only descriptive statistics on them.
Bottom line
mssense-eval-benchmark v1.1 supports detection of:
- 5–10 % absolute differences on the full corpus for RQ1 / RQ2 / RQ4
- per-cell estimates with ±10 % to ±15 % 95 % CI for RQ3
- non-degenerate per-label CIs for all six
expected_decisionlabels after the V5.15.h augmentation
These figures exceed the manuscript's Section 8.1 target of 1000 samples and are consistent with the JOT-published precedent of dataset papers at smaller scale (XCorpus 76 programs, JOT 2017).