statistical-testing
Statistical Testing provides researchers with advanced methods for hypothesis testing, Bayesian inference, survival analysis, time series modeling, and meta-analysis. The skill covers multiple comparison corrections, effect size calculations, and bootstrap/permutation approaches with APA-compliant reporting standards.
Statistical Testing performs advanced hypothesis testing, Bayesian analysis, survival analysis, time series forecasting, and meta-analysis.
AI-generated summary based on this skill's SKILL.md
Decision gist · record as of 2026-06-08
Statistical Testing performs advanced hypothesis testing, Bayesian analysis, survival analysis, time series forecasting, and meta-analysis. Statistical Testing provides researchers with advanced methods for hypothesis testing, Bayesian inference, survival analysis, time series modeling, and meta-analysis. The skill covers multiple comparison corrections, effect size calculations, and bootstrap/permutation approaches with APA-compliant reporting standards.
Use it when
- Statistical Testing provides Bayesian inference with prior specification, posterior estimation, and credible interval computation.
- Yes, Statistical Testing includes comprehensive survival analysis tools for time-to-event data.
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Install
beita6969/ScienceClaw/statistical-testing · repository language: TypeScript
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Frequently asked questions
AI-generated answers based on this skill's SKILL.md and metadata
How to do hypothesis testing in Python with statistical-testing?
Statistical Testing enables advanced hypothesis testing through multiple frameworks. You can perform parametric tests (t-tests, ANOVA), non-parametric alternatives (Mann-Whitney U, Kruskal-Wallis), and apply multiple comparison corrections like FDR to control Type I error across tests. The skill supports APA-compliant reporting of p-values, confidence intervals, and effect sizes.
What Bayesian analysis capabilities does statistical-testing offer?
Statistical Testing provides Bayesian inference with prior specification, posterior estimation, and credible interval computation. You can conduct Bayesian hypothesis testing using Bayes factors for model comparison, update beliefs with observed data, and interpret posterior distributions for parameter estimation with full uncertainty quantification.
Can statistical-testing handle survival analysis with censored data?
Yes, Statistical Testing includes comprehensive survival analysis tools for time-to-event data. Features include Kaplan-Meier curves, log-rank tests for group comparisons, Cox regression for hazard modeling, and proper handling of censored observations. These methods are essential for clinical trials and reliability studies.
How does statistical-testing support time series forecasting?
Statistical Testing covers time series analysis including stationarity testing, seasonal decomposition, and ARIMA modeling for forecasting. You can identify trend and seasonal components, fit autoregressive models, and generate predictions with confidence intervals for future periods.
What meta-analysis and effect size features are included?
Statistical Testing enables meta-analysis by combining effect sizes across multiple studies with proper weighting. Calculate Cohen's d, odds ratios, and other standardized effect measures. The skill supports forest plots, heterogeneity assessment, and fixed/random effects models for synthesizing research evidence.
Does statistical-testing support bootstrap and permutation methods?
Statistical Testing includes resampling approaches like bootstrap confidence intervals and permutation tests for distribution-free inference. These methods provide robust alternatives when parametric assumptions fail, enabling valid statistical conclusions without relying on normality or other strict distributional requirements.
SKILL.md
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Statistical Testing
Advanced statistical methods for scientific research. Venv: source /Users/zhangmingda/clawd/.venv/bin/activate
Bayesian Analysis
```python
Simple Bayesian estimation (conjugate priors)
import numpy as np from scipy import stats
Beta-Binomial (proportions)
Prior: Beta(alpha_prior, beta_prior), Data: k successes in n trials
alpha_prior, beta_prior = 1, 1 # uniform prior k, n = 45, 100 alpha_post = alpha_prior + k beta_post = beta_prior + (n - k) posterior = stats.beta(alpha_post, beta_post) print(f"Posterior mean: {posterior.mean():.3f}") print(f"95% credible interval: {posterior.ppf([0.025, 0.975])}")
Bayes Factor (BF10)
(truncated - see the full file via the links below)
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skills/statistical-testing/SKILL.md
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