Results for “test”

Design statistically valid A/B tests with hypothesis frameworks, sample size calculations, and analysis checklists.

Compute the Kruskal-Wallis H-test using scipy.stats.kruskal for independent samples, returning the H statistic and p-value.

Guides setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

Generate realistic dummy datasets for testing with customizable columns, constraints, and output formats (CSV, JSON, SQL, Python script).

Analyze A/B test results with statistical significance, sample size validation, confidence intervals, and ship/extend/stop recommendations.

Reimplements DnaSP 6 for population genetics analysis of aligned DNA sequences, including nucleotide diversity, haplotype statistics, neutrality tests, linkage disequilibrium, recombination, mismatch distribution, InDel polymorphism, between-population divergence, outgroup-based tests, HKA test, McDonald-Kreitman.

Guides statistical hypothesis testing with assumption checks, effect sizes, power analysis, Bayesian alternatives, and APA-formatted reporting for research data.

Manages and monitors dbt projects, model runs, and test results via dbt CLI and dbt Cloud API, covering run status, test failures, source freshness, and manifest analysis.

Provides dbt patterns for data transformation and analytics engineering, including model structures, incremental models, and testing.

Fetches and summarizes TestFlight crash reports, beta feedback, and performance diagnostics using the asc CLI.

Analiza datasets con Pandas y NumPy: explora distribuciones, correlaciones y patrones, y aplica tests de hipótesis para extraer conocimiento no obvio.

Performs independent-sample t-tests in R on numeric variables between subgroups within each main group, using user-specified column names and outputting statistical results.

Transforms A/B test and product experiment data into actionable readouts with hypothesis, metrics, interpretation, and decision.

Create, review, debug, test, or reproduce Python Jupyter notebooks by inspecting format, executing cells top-to-bottom in a clean kernel, and verifying outputs.

Provides expert guidance on modern Julia 1.10+ development, covering performance optimization, multiple dispatch, tooling, testing, and production-ready practices.

Analiza series temporales: tendencia, estacionalidad y pronóstico con Prophet, statsmodels y ML, incluyendo descomposición, tests de estacionariedad y evaluación contra baselines.

Performs design operations in the big-data domain, including hypothesis testing, statistical analysis, and data visualization using ML frameworks.

Analyzes .NET project code coverage and CRAP (Change Risk Anti-Patterns) scores to identify risk hotspots, methods blocking coverage gains, and prioritize where to add tests.

Analyzes game balance by extracting stats from code and running mathematical simulations, including DPS tier lists, TTK matrices, EHP comparisons, economy stress tests, progression pacing, and Monte Carlo RNG simulations.

Calculates the mean, standard deviation, and P-value for user-provided numerical datasets, with optional result-only output.

Manage Arize datasets and examples using the ax CLI: create, list, get, export, and append datasets for evaluation and experimentation.

Write, review, debug, test, and optimize Python Polars code with version-grounded object types, schemas, and execution boundaries.

Mines git history to compute development quality, velocity, and stability metrics, compares them against stored baselines, and tracks improvement over time.

Write, review, debug, test, or optimize pandas Series, DataFrame, Index, groupby, merge, reshape, dtype, missing-value, and time-series code.

Performs differential expression analysis on bulk RNA-seq or pseudo-bulk count matrices with QC, PCA, and contrast testing.

Produces NetworkX code with explicit graph kind, node identity, edge multiplicity, direction, attribute schema, weight semantics, and algorithm preconditions, including testing.

Fit statistical models (OLS, GLM, ARIMA, mixed models) with detailed diagnostics, residuals, and inference for econometrics and time series analysis.

Provides expert guidance for systematic backtesting of trading strategies, covering methodology, stress testing, bias prevention, and result interpretation.

Automates genome-wide association studies from genotype files to publication-ready results, running PLINK2 QC and REGENIE regression with Manhattan and QQ plots.

Perform differential gene expression analysis for bulk RNA-seq data using PyDESeq2, supporting formulaic designs, Wald tests, FDR correction, LFC shrinkage, and result visualization.