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Found 27 Skills
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'.
Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.
Statistics, probability, linear algebra, and mathematical foundations for data science
Guide product managers through Jeff Gothelf's Lean UX Canvas v2—a one-page tool that frames work around a business problem, exposes assumptions, and ensures learning every sprint.
Statistical analysis toolkit. Hypothesis tests (t-test, ANOVA, chi-square), regression, correlation, Bayesian stats, power analysis, assumption checks, APA reporting, for academic research.
Use when asked to calculate statistical power, determine sample size, or plan experiments for hypothesis testing.
Apply statistical methods including descriptive stats, trend analysis, outlier detection, and hypothesis testing. Use when analyzing distributions, testing for significance, detecting anomalies, computing correlations, or interpreting statistical results.
Meta-cognitive reasoning specialist for evidence-based analysis, hypothesis testing, and cognitive failure prevention. Use when conducting reviews, making assessments, debugging complex issues, or any task requiring rigorous analytical reasoning. Prevents premature conclusions, assumption-based errors, and pattern matching without verification.
Designs an A/B test or experiment with clear hypothesis, variants, success metrics, sample size, and duration. Use when planning experiments to validate product changes or test hypotheses.
Use when making predictions or judgments under uncertainty and need to explicitly update beliefs with new evidence. Invoke when forecasting outcomes, evaluating probabilities, testing hypotheses, calibrating confidence, assessing risks with uncertain data, or avoiding overconfidence bias. Use when user mentions priors, likelihoods, Bayes theorem, probability updates, forecasting, calibration, or belief revision.
Define a Proof of Life (PoL) probe—a lightweight validation artifact that surfaces harsh truths before expensive development. Use it to test hypotheses with minimal investment.
Apply statistical methods to financial data including descriptive statistics, covariance estimation, regression, hypothesis testing, and resampling. Use when the user asks about return distributions, correlation between assets, building a covariance matrix, running a CAPM regression, testing whether alpha is significant, checking if returns are normal, or estimating confidence intervals. Also trigger when users mention 'volatility', 'how correlated are these', 'fat tails', 'skewness', 'R-squared', 'beta of a fund', 'bootstrap a Sharpe ratio', 'shrinkage estimator', 'Ledoit-Wolf', or ask why their optimizer produces unstable weights.