Requirements
- Target platform
- OpenClaw
- Install method
- Manual import
- Extraction
- Extract archive
- Prerequisites
- OpenClaw
- Primary doc
- SKILL.md
Tencent SkillHub · Data Analysis
Statistics
Build statistical intuition from basic probability to advanced inference.
skill openclawclawhub Free
Build statistical intuition from basic probability to advanced inference.
⬇ 0 downloads ★ 0 stars Unverified but indexed
Install for OpenClaw
Quick setup
- Download the package from Yavira.
- Extract the archive and review SKILL.md first.
- Import or place the package into your OpenClaw setup.
Package facts
- Download mode
- Yavira redirect
- Package format
- ZIP package
- Source platform
- Tencent SkillHub
- What's included
- SKILL.md
Validation
- Use the Yavira download entry.
- Review SKILL.md after the package is downloaded.
- Confirm the extracted package contains the expected setup assets.
Install with your agent
Agent handoff
Hand the extracted package to your coding agent with a concrete install brief instead of figuring it out manually.
- Download the package from Yavira.
- Extract it into a folder your agent can access.
- Paste one of the prompts below and point your agent at the extracted folder.
New install
I downloaded a skill package from Yavira. Read SKILL.md from the extracted folder and install it by following the included instructions. Tell me what you changed and call out any manual steps you could not complete.
Upgrade existing
I downloaded an updated skill package from Yavira. Read SKILL.md from the extracted folder, compare it with my current installation, and upgrade it while preserving any custom configuration unless the package docs explicitly say otherwise. Summarize what changed and any follow-up checks I should run.
Trust & source
Release facts
- Source
- Tencent SkillHub
- Verification
- Indexed source record
- Version
- 1.0.0
Provenance
- Publisher
- ivangdavila
- Source page
- View original listing
- Canonical URL
- Open canonical page
Documentation
Detect Level, Adapt Everything
Context reveals level: notation familiarity, software mentioned, problem complexity When unclear, start with concrete examples and adjust based on response Never condescend to experts or overwhelm beginners
For Beginners: Intuition Before Formulas
Probability through physical objects — dice, coins, cards, colored balls in bags Averages as balance points — "If everyone shared equally, each would get..." Variation matters as much as center — two classes with same average, very different spreads Graphs before numbers — show the shape, then quantify it Sampling as tasting soup — one spoonful tells you about the pot if well stirred Correlation isn't causation — ice cream sales and drowning both rise in summer Connect to their decisions — weather forecasts, medical tests, sports statistics
For Students: Frameworks and Assumptions
Name the test AND its assumptions — normality, independence, equal variance Effect size alongside p-value — statistical significance ≠ practical importance Confidence intervals tell richer stories than hypothesis tests alone Distinguish population parameters from sample statistics — Greek vs Roman letters matter Simulation builds intuition — bootstrap, permutation tests show what formulas hide Regression diagnostics before interpretation — residual plots catch violations Bayesian vs frequentist — acknowledge the philosophical divide, explain context for each
For Researchers: Rigor and Honesty
Pre-registration prevents p-hacking — specify analysis before seeing data Power analysis before collecting — underpowered studies waste resources Multiple comparisons require adjustment — Bonferroni, FDR, or justify why not Report effect sizes and confidence intervals — not just p-values Missing data mechanisms matter — MCAR, MAR, MNAR require different treatments Causal inference needs design — DAGs, potential outcomes, state assumptions explicitly Reproducibility means code and data — "available upon request" is not reproducible
For Teachers: Common Misconceptions
p-value is NOT probability hypothesis is true — it's probability of data given null Failing to reject ≠ accepting null — absence of evidence isn't evidence of absence Large samples don't fix bias — garbage in, garbage out regardless of n Standard deviation vs standard error — population spread vs sampling precision Correlation coefficient hides nonlinearity — always plot first Use real messy data — textbook examples with clean answers mislead Teach skepticism — "How was this measured? Who was sampled? What's missing?"
Always
Visualize data before computing anything State assumptions explicitly — every test has them Distinguish exploratory from confirmatory — same data can't do both
Category context
Data access, storage, extraction, analysis, reporting, and insight generation.
Source: Tencent SkillHub
Largest current source with strong distribution and engagement signals.
Package contents
Included in package
1 Docs
- SKILL.md