Why Your LLM Agent Gives a Different P-Value Every Time (And What to Build Instead)
The article discusses the variability in p-values generated by LLMs when analyzing the same dataset. It highlights the issue of LLMs sometimes skipping necessary assumption checks, leading to different statistical tests being applied. The author proposes a solution that retains the LLM for decision-making while using a fixed computation engine for analysis.
- ▪LLMs can produce different p-values for the same dataset due to stochastic behavior in their analysis methods.
- ▪Only one out of five runs of an LLM checked for normality before choosing a statistical test, leading to significant differences in results.
- ▪The author suggests using LLMs for routing decisions while employing a validated computation engine for consistent analysis.
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| Original publisher | DEV.to (Top) |
| Canonical URL | https://dev.to/cheng-peng0718/why-your-llm-agent-gives-a-different-p-value-every-time-and-what-to-build-instead-5dc6 |
| Publication time | Wed, 03 Jun 2026 06:34:28 +0000 |
| Retrieval time | 2026-06-03T06:41:57.977Z |
| Last seen | 2026-06-03T06:41:57.977Z |
| Headline source | Publisher (no WeSearch rewrite) |
| Excerpt source | publisher body |
| Excerpt method | First ~120 words (~800 chars) of extracted publisher body, fair-use limited. |
| Summary | WeSearch · cerebras-chat (WeSearch summarizer) |
| Summary source text | contentText |
| Citation coverage | Summary is a WeSearch-generated derivative; primary citation is the original publisher URL. |
| Cluster | YAa68vGXJUc6 |
| Cluster logic | Grouped by semantic title/content similarity across sources within a rolling window. Same-publisher template collisions are excluded from coverage comparison. |
| Ranking reason | Story pages are not engagement-ranked. Hub feeds use recency, with optional source-diversified chronological ordering (cap consecutive stories per source). No personalized ranking. |
| Publisher visit | Yes — open original |
| Substitutes article? | No — link-out required for full text |
Rights status (four layers)
WeSearch handling by dimension
| Indexing | May the item be indexed (stored, ranked, made findable)? | Allowed |
| Snippet | May a short excerpt of the publisher's text be shown? | Allowed |
| AI summary | May WeSearch generate its own short summary of the article? | Limited |
| Retrieval / RAG | May the content be exposed for third-party retrieval-augmented generation? | Not asserted |
| Model training | May the content be used to train AI models? | Not asserted |
| Commercial reuse | May the content be reused commercially? | Not permitted |
Basis: Derived from the published RSS/Atom feed. Contact: [email protected]. Reviewed: 2026-07-24.
Opening excerpt (first ~120 words) tap to expand
try { if(localStorage) { let currentUser = localStorage.getItem('current_user'); if (currentUser) { currentUser = JSON.parse(currentUser); if (currentUser.id === 3965684) { document.getElementById('article-show-container').classList.add('current-user-is-article-author'); } } } } catch (e) { console.error(e); } Cheng Peng Posted on Jun 3 Why Your LLM Agent Gives a Different P-Value Every Time (And What to Build Instead) #python #llm #datascience #opensource Hand the same paired before/after dataset (n = 25) to ChatGPT five times. Same prompt: "These are the same subjects measured before and after an intervention. Did their scores change significantly?" Four of the five runs return p = 0.009 from a paired t-test.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at DEV.to (Top).
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