# Teradata F-Test

> F-test for comparing variances between two groups

- Skill: `teradata-labs/teradata-f-test` (Agent Skill, multi-file: 11 files)
- Install (CLI): `npx skillmds@latest add teradata-labs/teradata-f-test`
- Raw SKILL.md: https://api.skillmd.com/api/skills/teradata-labs/teradata-f-test/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: Data & Analytics
- Author: teradata-labs (https://skillmd.com/u/teradata-labs)
- Updated: 2026-09-10
- Page: https://skillmd.com/skills/teradata-labs/teradata-f-test

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# Teradata F-Test

| Property | Value |
|----------|-------|
| **Skill Name** | Teradata F-Test |
| **Description** | F-test for comparing variances between two groups |
| **Category** | Hypothesis Testing |
| **Primary Function** | TD_FTest |
| **Framework** | SQLE |

## Core Capabilities

- Automated table structure analysis via DBC.ColumnsV
- Dynamic SQL generation for TD_FTest
- Complete workflow from data preparation to results interpretation
- Data quality validation and preprocessing guidance
- Parameter optimization and tuning

## Key Parameters

- **TargetColumn**: Numeric column with sample data
- **GroupColumn**: Grouping column (two groups)
- **Significance**: Significance level (default 0.05)

## Use Cases

1. Compare variances between populations
2. Pre-test for t-test assumptions
3. Quality control variance analysis
4. Process consistency evaluation

## Example Usage

```sql
-- TD_FTest execution
SELECT * FROM TD_FTest (
    ON {USER_DATABASE}.{USER_TABLE} AS InputTable
    USING
    TargetColumn ('{NUMERIC_COLUMN}')
    GroupColumn ('{GROUP_COLUMN}')
    Significance (0.05)
) AS dt;
```

## Scripts Included

### Core Analytics Scripts
- **`table_analysis.sql`**: Automatic table structure discovery
- **`preprocessing.sql`**: Data preparation and feature engineering
- **`model_training.sql`**: TD_FTest execution
- **`evaluation.sql`**: Results analysis and metrics
- **`complete_workflow_template.sql`**: End-to-end workflow

### Utility Scripts
- **`data_quality_checks.sql`**: Comprehensive data validation
- **`parameter_tuning.sql`**: Parameter optimization
- **`diagnostic_queries.sql`**: Results diagnostics and interpretation

## Best Practices

- Always run table_analysis.sql first to understand your data structure
- Validate data quality before executing the analytical function
- Use parameter_tuning.sql to find optimal configuration
- Review diagnostic_queries.sql output for model/results validation

## Limitations

- Requires Teradata Vantage 17.20+ with ClearScape Analytics
- Input data must meet function-specific requirements
- Results depend on data quality and parameter configuration

*Teradata F-Test - ClearScape Analytics skill for Teradata Vantage*