Stat Hypothesis Testing

What it does

When your agent uses it

• The user needs to determine whether a result is statistically significant
• Choose the right statistical test
• Interpret p-values correctly
• Evaluate research findings — even if they say is this result significant

Example prompts

• “is this result significant”
• “which statistical test should I use”
• “what does this p-value mean”
• “/stat-hypothesis-testing”

Workflow steps

6 steps, taken from the first numbered list in SKILL.md.

1. 1State hypotheses: H₀ and H₁ with specific parameters
2. 2Choose test: Based on data type, distribution, and groups (use guide above)
3. 3Set α: Usually 0.05 (justify if different)
4. 4Calculate: Run the test, get test statistic and p-value
5. 5Decide: p < α → reject H₀; p ≥ α → fail to reject H₀
6. 6Report: Effect size + confidence interval + p-value (not just "significant")

What it can do on your machine

Read from SKILL.md and the folder at commit 4e7f4f8. It shows what the files ask for, not the result of running them.

• Tool permissions

  Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

  From allowed-tools in the SKILL.md frontmatter.

• Runs code

  No scripts in the folder and no shell commands in SKILL.md (its code samples are markdown).

  From the folder's file list and the shell code blocks in SKILL.md.

• Network

  No URLs in SKILL.md.

  From URLs in SKILL.md, links to its own repository left out.

• Credentials

  Names no API keys, tokens, secrets or passwords.

  From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Stat Hypothesis Testing loads about 1.1k tokens when it runs, and up to ~6.1k if it reads all its reference files. Until then it costs about 121 tokens; SKILL.md has 338 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it

~121

When it runs · the whole SKILL.md, loaded when a task matches

~1.1k

With references · SKILL.md plus every file in references/, read only if the agent opens them

~6.1k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from asgard-ai-platform/skills at commit 4e7f4f8, republished under its MIT licence (© asgard-ai-platform). 338 words, ~1,133 tokens.

name

stat-hypothesis-testing

description

Conduct statistical hypothesis testing including null/alternative hypothesis formulation, p-values, Type I/II errors, and test statistic selection. Use this skill when the user needs to determine whether a result is statistically significant, choose the right statistical test, interpret p-values correctly, or evaluate research findings — even if they say 'is this result significant', 'which statistical test should I use', or 'what does this p-value mean'.

metadata.category

WP-21 設計/資訊/傳播/公衛

metadata.tags

statistics, hypothesis-testing, p-value, inference

IRON LAW: Statistical Significance ≠ Practical Significance

A p-value < 0.05 means the result is unlikely under the null hypothesis.
It does NOT mean the result is important, large, or practically meaningful.
With a large enough sample, a 0.1% conversion rate difference becomes
"statistically significant" but is practically worthless.

ALWAYS report effect size alongside p-value.

IRON LAW: State Hypotheses BEFORE Looking at Data

H₀ (null) and H₁ (alternative) must be defined before data analysis.
Choosing hypotheses after seeing the data = p-hacking = scientific fraud.
"We found an interesting pattern, let's test it on the same data" is invalid.

# Hypothesis Test: {Research Question}

## Hypotheses
- H₀: {null — no effect/difference}
- H₁: {alternative — there IS an effect/difference}
- α = {0.05 or other}

## Test Selection
- Test: {name}
- Rationale: {why this test fits the data}
- Assumptions checked: {normality, independence, equal variance}

## Results
- Test statistic: {value}
- p-value: {value}
- Effect size: {value and interpretation}
- 95% CI: [{lower}, {upper}]

## Decision
{Reject / Fail to reject H₀}

## Interpretation
{What this means in practical terms, with effect size context}

© asgard-ai-platform, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 3 other files (references) in stat-hypothesis-testing of asgard-ai-platform/skills.

• SKILL.md
• examples/sample_scenario.md
• references/nonparametric-tests.md
• references/sample-size.md

Open the folder on GitHubat commit 4e7f4f8