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 agentmods add skills/danielrosehill/claude-data-analyst-plugin/hypothesis-testingnpx skills add danielrosehill/Claude-Data-Analyst-plugin --skill hypothesis-testinggit clone --depth 1 https://github.com/danielrosehill/Claude-Data-Analyst-pluginWrote 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/danielrosehill/claude-data-analyst-plugin/hypothesis-testing)<a href="https://agentmods.dev/skills/danielrosehill/claude-data-analyst-plugin/hypothesis-testing"><img src="https://agentmods.dev/badge/skills/danielrosehill/claude-data-analyst-plugin/hypothesis-testing.svg" alt="Measured on agentmods" height="20"></a>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 | $0.00056 | $0.00812 |
| Opus 5 | $0.00028 | $0.00406 |
| Sonnet 5 | $0.00011 | $0.00162 |
| Haiku 4.5 | $0.00006 | $0.00081 |
Grade A, and why
hypothesis-testing 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 4d 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.
How it starts
The opening of the file, as written. The whole thing — 71 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Hypothesis Testing
Rigorous first-pass evaluation of a user hypothesis against a dataset.
Inputs
- Path to a dataset file or folder.
- The hypothesis, stated in natural language (e.g. "customers in segment A spend more than those in segment B", "there's no difference in conversion by landing page").
- Optional: desired significance level α (default 0.05).
Step 1 — Formalise the hypothesis
Before testing, restate the user's claim as:
- H₀ (null): typically "no effect" / "no difference" / "no relationship".
- H₁ (alternative): the user's claim, one- or two-sided as appropriate.
- Variables involved: identify which columns map to the hypothesis, with their dtypes.
- Test type selected based on the data shape (see decision table below).
Show this formalisation to the user and proceed unless they object.
Step 2 — Select a test
| User hypothesis shape | Variables | Default test |
|---|---|---|
| Group A mean ≠ Group B mean | 1 numeric + 1 binary categorical | Welch's t-test (unequal var) or Mann-Whitney if non-normal |
| Difference across 3+ groups | 1 numeric + 1 categorical | One-way ANOVA or Kruskal-Wallis |
| Association between two categoricals | 2 categoricals | Chi-square (or Fisher's exact if small cells) |
| Correlation ≠ 0 | 2 numerics | Pearson (linear) or Spearman (monotonic) |
| Proportion differs from value | 1 binary | One-sample proportion z-test |
| Paired before/after | 1 numeric, paired | Paired t-test or Wilcoxon signed-rank |
| Time trend | 1 numeric + time | Mann-Kendall or regression slope test |
Check assumptions (normality via Shapiro-Wilk for small n or visual for large n; equal variance via Levene's) and fall back to the non-parametric counterpart when they fail.
Step 3 — Execute
Recommended: uv run --with pandas --with scipy --with statsmodels python -c '...'.
Report:
- Sample size(s)
- Effect size (Cohen's d, r, Cramér's V, odds ratio — whichever fits)
- Test statistic and p-value
- 95% confidence interval for the effect
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.
- 4d ago First seen · 71 lines · 56 tokens per session scan A 86b6b23ed7b4
hypothesis-testing is a skill published in the GitHub repository danielrosehill/Claude-Data-Analyst-plugin (11 stars, last pushed 4mo ago), licensed MIT. It adds 56 tokens to every session and 812 once invoked, about $0.0003 per session on Opus 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
ppt-analysis
PPT (.pptx/.ppt) 全量解析。覆盖：所有 slide 文本/表格/图表提取、嵌入图片 caption、纯图片 slide 渲染识别、数据标签提取。.
large-file-conditional-formatting
根据Excel总行数自动切换Parquet加速读取，计算特定维度的时间序列平均值，并使用openpyxl输出带有条件格式（如低于均值标绿）和自定义样式的分析报告。.
trend-analysis
基于多维度数据进行分级评估与趋势预测，通过设定差异化增长率计算预测值，并生成对比可视化图表，适用于绩效评估、目标设定等场景。.
outlier-detection-and-quality-assessment
执行全面的异常值检测与数据质量评估，利用 IQR 方法识别异常值并结合偏度、峰度分析数据分布特征，适用于非正态分布数据的预处理阶段。.
pie-chart-data-analysis
Step1 读取文件并统计所有 Sheet 的行数，确认数据规模以决定处理策略。.
multi-sheet-reading-and-analysis
用于读取多工作表Excel文件，动态评估数据量以启用Parquet大文件优化，并执行正则清洗、分类汇总、线性拟合及生成带格式的图表与结果文件。.