statistical-analysis
by K-Dense-AIThe statistical-analysis skill helps you choose, run, and report defensible tests for Data Analysis, including assumptions, effect sizes, power, and APA-style results. Use it for academic research, experiments, and observational studies when test selection and clear reporting matter more than coding a specific model.
This skill scores 74/100, which means it is acceptable to list for directory users as a real statistical-analysis workflow aid, but not a top-tier install choice. The repository gives enough substance to help an agent choose tests, check assumptions, and format APA-style reporting, though users should expect some limits in operational packaging and integration guidance.
- Clear trigger language for hypothesis tests, regression/correlation, Bayesian analysis, assumption checking, power analysis, and APA reporting.
- Substantial skill content with many headings and explicit workflow sections, which should help an agent navigate the analysis steps with less guesswork.
- No placeholder markers and no fatal structural issues; the skill body appears to contain real procedure-oriented guidance rather than a stub.
- No install command or support files/scripts are provided, so adoption depends entirely on reading SKILL.md and may require manual interpretation.
- Experimental/test signal and lack of references/resources reduce trust for users who want validated methods, examples, or reproducible implementation guidance.
Overview of statistical-analysis skill
The statistical-analysis skill helps you choose, run, and report the right statistical test for a research question, with attention to assumptions, effect sizes, power, and APA-style output. It is most useful for Data Analysis workflows where the main problem is not “compute a model,” but “decide what analysis is defensible and how to explain it clearly.”
Who this skill is best for
Use this statistical-analysis skill if you need support for academic research, thesis work, experiment reporting, or observational data analysis where test choice matters. It fits users who have data and a question, but are not fully confident about test selection, assumption checks, or reporting format.
What it helps you do
The core job is to move from a messy research question to an analysis plan: what test to use, what assumptions to verify, what effect size or power issue matters, and how to present the result. That makes the skill more useful than a generic prompt when you need statistical-analysis for Data Analysis that is methodologically sound.
Main limits to know
This is guidance-oriented, not a replacement for purpose-built software like statsmodels when you need programmatic model implementation. It is also not ideal if your task is mainly data cleaning, dashboarding, or production ML rather than statistical inference and reporting.
How to Use statistical-analysis skill
Install it and confirm the skill scope
Use the repository install flow your environment supports, for example: npx skills add K-Dense-AI/claude-scientific-skills --skill statistical-analysis. After install, confirm that the active scope is the statistical-analysis skill and not a broader scientific-skills prompt that could blur the analysis decision path.
Give the skill a decision-ready prompt
The best statistical-analysis usage starts with a prompt that includes your research question, outcome variable, predictors or groups, sample size, data type, and any constraints. A weak prompt says “analyze my data.” A stronger one says: “I have 42 participants, a continuous outcome, two independent groups, and I need to know whether an independent-samples t-test is appropriate, what assumptions to check, and how to report the result in APA format.”
Read the right files first
Start with SKILL.md to understand the intended workflow, then inspect any linked sections that define test selection, assumption checking, and reporting conventions. If the repo includes only a single skill file, focus on the headings and examples inside it; there are no extra support folders to rely on here.
Use the skill as a workflow, not a one-shot answer
For best results, ask for the analysis plan first, then ask for assumptions, then ask for the final reporting language. That sequence reduces bad early choices and is especially helpful when the input is incomplete, the design is mixed, or the analysis could reasonably be done more than one way.
statistical-analysis skill FAQ
Is statistical-analysis skill only for academics?
No. It is strongest in academic and research settings, but it is also useful anywhere you need statistically defensible test selection, assumption checking, or clear interpretation for Data Analysis.
Do I still need ordinary prompts if I install it?
Yes, but the prompt becomes much more targeted. The statistical-analysis skill gives you a better default workflow than a generic prompt, especially when you need power analysis, test selection, or APA reporting instead of a broad explanation.
When should I not use this skill?
Do not use it when you need to write code for a specific modeling library, when the task is mainly exploratory data wrangling, or when you only want a quick intuitive summary without methodological detail. In those cases, a simpler prompt or a different tool may be faster.
Is it beginner-friendly?
Yes, if you can provide basic study details. The main beginner risk is underspecifying the design, which leads to the wrong test or weak assumptions. If you can name your variables and groups clearly, the skill is a good fit.
How to Improve statistical-analysis skill
Provide the analysis context the model cannot infer
The biggest quality jump comes from specifying the study design. Include whether groups are independent or paired, whether outcomes are continuous or categorical, sample sizes per group, missing data, and any repeated-measures structure. Those details materially change statistical-analysis recommendations.
Ask for the decision chain, not just the result
Instead of asking only for the final test, request the reasoning path: “recommend the test, explain why it fits, list assumptions, and show APA wording.” That helps the statistical-analysis skill surface hidden tradeoffs and makes the output easier to trust.
Share constraints that affect test choice
Mention non-normality, unequal variances, small samples, multiple comparisons, clustered data, or ordinal measurements. These constraints often determine whether the right answer is a standard parametric test, a robust alternative, or a different reporting approach.
Iterate on the first draft
If the first answer is too broad, refine it by asking for one study design only, one outcome only, or one reporting standard only. The best statistical-analysis guide output comes from narrowing scope after the first pass, then asking for a cleaner recommendation, a stronger assumption check, or a tighter APA-ready summary.