Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add wentorai/research-plugins --skill hypothesis-testing-guidegit clone --depth 1 https://github.com/wentorai/research-pluginsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/wentorai/research-plugins/hypothesis-testing-guide)<a href="https://agentmods.dev/skills/wentorai/research-plugins/hypothesis-testing-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/hypothesis-testing-guide/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/wentorai/research-plugins/hypothesis-testing-guide"><img src="https://agentmods.dev/badge/skills/wentorai/research-plugins/hypothesis-testing-guide.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00016 | $0.02167 |
| Opus 5.5 | $0.00006 | $0.00867 |
| Sonnet 5.5 | $0.00003 | $0.00433 |
| Haiku 4.5 | $0.00002 | $0.00217 |
Grade A, and why
hypothesis-testing-guide scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 1mo ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
Copies of this mod
1 near-identical copy found in the catalogue:
- research-hypothesis-testing — 89% identical, 1 lines differ
How it starts
The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hypothesis Testing Guide
Overview
Hypothesis testing is the backbone of empirical research. It provides a principled framework for deciding whether observed differences in data reflect genuine effects or merely random variation. Misuse of hypothesis tests -- p-hacking, ignoring assumptions, confusing statistical and practical significance -- is a leading cause of irreproducible findings.
This guide covers the core hypothesis testing framework, the most commonly used tests across disciplines, assumption checking, effect size reporting, power analysis for sample size planning, and multiple comparison corrections. Each test is accompanied by Python code using scipy, statsmodels, and pingouin, ready to integrate into research workflows.
The goal is not just to help you run tests, but to help you run the right test correctly and report results following modern standards (APA 7th edition, journal best practices).
The Hypothesis Testing Framework
Step-by-Step Procedure
- State hypotheses. Define H0 (null: no effect) and H1 (alternative: effect exists).
- Choose significance level. Typically alpha = 0.05, but justify your choice.
- Select the appropriate test. Based on data type, distribution, and design.
- Check assumptions. Normality, homogeneity of variance, independence.
- Compute test statistic and p-value.
- Report effect size and confidence interval. p-values alone are insufficient.
- Make a decision. Reject or fail to reject H0, with practical interpretation.
Common Errors
| Error Type | Definition | Probability |
|---|---|---|
| Type I (False Positive) | Reject H0 when it is true | alpha (usually 0.05) |
| Type II (False Negative) | Fail to reject H0 when it is false | beta (usually 0.20) |
| Power | Probability of correctly detecting an effect | 1 - beta (target: 0.80) |
Test Selection Guide
| Research Question | Data Type | Groups | Test |
|---|---|---|---|
| Two group means differ? | Continuous, normal | 2 independent | Independent t-test |
| Before/after difference? | Continuous, normal | 2 paired | Paired t-test |
| Multiple group means differ? | Continuous, normal | 3+ independent | One-way ANOVA |
| Two group medians differ? | Ordinal / non-normal | 2 independent | Mann-Whitney U |
| Before/after (non-normal)? | Ordinal / non-normal | 2 paired | Wilcoxon signed-rank |
| Multiple groups (non-normal)? | Ordinal / non-normal | 3+ independent | Kruskal-Wallis |
| Association between categories? | Categorical | 2 variables | Chi-square test |
| Correlation? | Continuous | 2 variables | Pearson or Spearman |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 1mo ago First seen · 211 lines · 16 tokens per session scan A 95c1dc213434
hypothesis-testing-guide is a skill published in the GitHub repository wentorai/research-plugins (306 stars, last pushed 4mo ago), licensed MIT. It adds 16 tokens to every session and 2,167 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
instrument-data-to-allotrope
Convert laboratory instrument output files (PDF, CSV, Excel, TXT) to Allotrope Simple Model (ASM) JSON format or flattened 2D CSV. Use this skill when scientists need to standardize instrument data for LIMS systems, data lakes, or downstream analysis. Supports auto-detection of instrument types. Outputs include full…
exploratory-data-analysis
Performs bounded, local exploratory analysis of explicitly supported scientific files. Supports redacted CSV/TSV/JSON profiles; optional NumPy, HDF5, FASTA/FASTQ, and basic image metadata inspection; missingness/leakage audits; outlier and transformation sensitivity; and rigorous EDA report scaffolds. Other domain…
matlab
Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
phylogenetics
Builds and analyzes phylogenetic trees using MAFFT multiple sequence alignment, IQ-TREE maximum likelihood with ModelFinder and branch support, and FastTree approximate inference. Uses ETE3 for tree summaries and visualization. Applies to homologous nucleotide or protein sequences, microbial gene trees, protein…
research-engineer
An uncompromising Academic Research Engineer. Operates with absolute scientific rigor, objective criticism, and zero flair. Focuses on theoretical correctness, formal verification, and optimal implementation across any required technology.
presenting-conference-talks
Generates conference presentation slides (Beamer LaTeX PDF and editable PPTX) from a compiled paper with speaker notes and talk script. Use when preparing oral talks, spotlight presentations, or invited talks for ML and systems conferences.