Analyzing Data
by Git-Fg
Statistical analysis toolkit for hypothesis testing, regression, correlation, Bayesian statistics, power analysis, and APA reporting. USE when conducting academic research, analyzing experimental data, testing hypotheses with t-tests or ANOVA, performing regression analyses, calculating effect sizes, checking statistical assumptions, or generating publication-ready statistical reports. Do not use for literature reviews, tool selection, or methodology design → see conducting-research skill.
Skill Details
Repository Files
9 files in this skill directory
name: analyzing-data description: "Statistical analysis toolkit for hypothesis testing, regression, correlation, Bayesian statistics, power analysis, and APA reporting. USE when conducting academic research, analyzing experimental data, testing hypotheses with t-tests or ANOVA, performing regression analyses, calculating effect sizes, checking statistical assumptions, or generating publication-ready statistical reports. Do not use for literature reviews, tool selection, or methodology design → see conducting-research skill." allowed-tools: [Read, Write, Edit, Bash]
Statistical Analysis Protocol
When to Use This Skill
This skill should be used when:
- Conducting statistical hypothesis tests (t-tests, ANOVA, chi-square)
- Performing regression or correlation analyses
- Running Bayesian statistical analyses
- Checking statistical assumptions and diagnostics
- Calculating effect sizes and conducting power analyses
- Reporting statistical results in APA format
- Analyzing experimental or observational data for research
Core Capabilities
- Test Selection and Planning: Choose appropriate tests and compute power. See references/test_selection_guide.md.
- Assumption Checking: Verify normality, homogeneity, etc. See references/assumptions_and_diagnostics.md.
- Statistical Testing: hypothesis testing, regression, correlation, Bayesian. See references/analysis-examples.md.
- Effect Sizes: Calculate and interpret. See references/effect_sizes_and_power.md.
- Reporting: APA-style reports. See references/reporting_standards.md.
Workflow Decision Tree
Use this decision tree to determine your analysis path:
START
│
├─ Need to SELECT a statistical test?
│ └─ YES → See [references/test_selection_guide.md](references/test_selection_guide.md)
│ └─ NO → Continue
│
├─ Ready to check ASSUMPTIONS?
│ └─ YES → See [references/assumptions_and_diagnostics.md](references/assumptions_and_diagnostics.md)
│ └─ NO → Continue
│
├─ Ready to run ANALYSIS?
│ └─ YES → See [references/analysis-examples.md](references/analysis-examples.md)
│ └─ NO → Continue
│
└─ Need to REPORT results?
└─ YES → See [references/report-templates.md](references/report-templates.md)
Resources
- references/test_selection_guide.md: Decision tree for choosing tests.
- references/assumptions_and_diagnostics.md: Guidance on assumption checks.
- references/effect_sizes_and_power.md: Effect sizes and power analysis.
- references/bayesian_statistics.md: Bayesian methods.
- references/reporting_standards.md: APA-style reporting.
- references/analysis-examples.md: Code examples for tests.
- references/report-templates.md: Report templates.
Scripts
- scripts/assumption_checks.py: Automated assumption checking tools.
Related Skills
Xlsx
Comprehensive spreadsheet creation, editing, and analysis with support for formulas, formatting, data analysis, and visualization. When Claude needs to work with spreadsheets (.xlsx, .xlsm, .csv, .tsv, etc) for: (1) Creating new spreadsheets with formulas and formatting, (2) Reading or analyzing data, (3) Modify existing spreadsheets while preserving formulas, (4) Data analysis and visualization in spreadsheets, or (5) Recalculating formulas
Clickhouse Io
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
Clickhouse Io
ClickHouse database patterns, query optimization, analytics, and data engineering best practices for high-performance analytical workloads.
Analyzing Financial Statements
This skill calculates key financial ratios and metrics from financial statement data for investment analysis
Data Storytelling
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
Team Composition Analysis
This skill should be used when the user asks to "plan team structure", "determine hiring needs", "design org chart", "calculate compensation", "plan equity allocation", or requests organizational design and headcount planning for a startup.
Kpi Dashboard Design
Design effective KPI dashboards with metrics selection, visualization best practices, and real-time monitoring patterns. Use when building business dashboards, selecting metrics, or designing data visualization layouts.
Dbt Transformation Patterns
Master dbt (data build tool) for analytics engineering with model organization, testing, documentation, and incremental strategies. Use when building data transformations, creating data models, or implementing analytics engineering best practices.
Sql Optimization Patterns
Master SQL query optimization, indexing strategies, and EXPLAIN analysis to dramatically improve database performance and eliminate slow queries. Use when debugging slow queries, designing database schemas, or optimizing application performance.
Anndata
This skill should be used when working with annotated data matrices in Python, particularly for single-cell genomics analysis, managing experimental measurements with metadata, or handling large-scale biological datasets. Use when tasks involve AnnData objects, h5ad files, single-cell RNA-seq data, or integration with scanpy/scverse tools.