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name: ds-statistical-power compatibility: opencode completeness: 95 content-types:

• code
• guidance
• do-dont
• examples description: '"Analyzes statistical power, sample size determination, effect size estimation, and Type I/Type II error control"' license: MIT maturity: stable metadata: domain: coding output-format: code related-skills: ds-ab-testing, ds-experimental-design, ds-hypothesis-testing role: implementation scope: implementation triggers: statistical power, power analysis, sample size, effect size, Type I error Type II error archetypes:
  • tactical
  • generation anti_triggers:
  • brainstorming
  • vague ideation
  • code golf
  • over-engineering response_profile: verbosity: low directive_strength: high abstraction_level: operational version: "1.0.0"

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Statistical Power

Comprehensive guide to statistical power in machine learning and data science workflows.

When to Use This Skill

• Solving real-world experimentation & a/b testing problems
• Building machine learning pipelines with statistical power
• Implementing best practices for statistical power
• Optimizing model performance using statistical power techniques
• Learning industry-standard approaches to statistical power

When NOT to Use This Skill

• When using pre-built libraries without understanding underlying concepts
• For toy problems that don't require statistical power rigor
• When domain expertise in specific problem requires different approach
• If your problem doesn't require the complexity this skill provides

Purpose and Key Concepts

Statistical Power is a critical component of the machine learning workflow. This skill covers:

1. Theoretical foundations — Mathematical principles and statistical concepts
2. Practical implementation — Working code examples and patterns
3. Common pitfalls — Mistakes to avoid and how to recover from them
4. Best practices — Industry-standard approaches and optimization techniques

Core Workflow

1. Understand the problem — Clearly define what you're solving for
2. Select approach — Choose the right technique for your data and constraints
3. Implement solution — Write clean, tested code following best practices
4. Validate results — Verify your implementation with tests and validation
5. Optimize performance — Improve efficiency and accuracy incrementally

Implementation Patterns

Pattern 1: Basic Statistical Power

import numpy as np
from statsmodels.stats.power import TTestPower

def calculate_basic_power(effect_size: float, sample_size: int, alpha: float = 0.05) -> float:
    """Calculate statistical power for a two-sample t-test."""
    if sample_size <= 0 or alpha <= 0 or alpha >= 1:
        raise ValueError("Invalid parameters: sample_size must be > 0, alpha in (0, 1)")
    
    power_analysis = TTestPower()
    power = power_analysis.power(effect_size=effect_size, nobs=sample_size, alpha=alpha)
    return float(np.clip(power, 0.0, 1.0))

# Example usage
if __name__ == "__main__":
    es: float = 0.5  # Medium effect size (Cohen's d)
    n: int = 50      # Sample size per group
    alpha: float = 0.05
    calculated_power: float = calculate_basic_power(es, n, alpha)
    print(f"Statistical Power: {calculated_power:.4f}")

Pattern 2: Production-Ready Statistical Power

import logging
import numpy as np
import pandas as pd
from typing import Any, Dict, Literal
from statsmodels.stats.power import TTestPower

logger = logging.getLogger(__name__)

class StatisticalPowerAnalyzer:
    """Production-grade statistical power analysis tool."""
    
    def __init__(self, alpha: float = 0.05, alternative: Literal['two-sided', 'larger', 'smaller'] = 'two-sided') -> None:
        self.alpha: float = alpha
        self.alternative: str = alternative
        self.ttest: TTestPower = TTestPower()
        
    def calculate_power(self, effect_size: float, sample_size: int) -> float:
        """Calculate power given effect size and sample size."""
        if sample_size <= 0 or effect_size < 0:
            raise ValueError("Sample size must be positive and effect size non-negative")
        return float(self.ttest.power(effect_size=effect_size, nobs=sample_size, alpha=self.alpha))
        
    def calculate_sample_size(self, effect_size: float, target_power: float) -> int:
        """Calculate required sample size for target power."""
        if target_power <= 0 or target_power >= 1:
            raise ValueError("Target power must be between 0 and 1")
        nobs: float = self.ttest.solve_power(effect_size=effect_size, power=target_power, alpha=self.alpha)
        return int(np.ceil(nobs))
        
    def execute(self, data: pd.DataFrame, target_power: float = 0.8) -> Dict[str, Any]:
        """Execute power analysis on provided data."""
        if data.empty:
            raise ValueError("Input DataFrame cannot be empty")
            
        group_a: pd.Series = data['group_a'].dropna()
        group_b: pd.Series = data['group_b'].dropna()
        pooled_std: float = np.sqrt(((len(group_a) - 1) * group_a.var() + (len(group_b) - 1) * group_b.var()) / (len(group_a) + len(group_b) - 2))
        effect_size: float = abs(group_a.mean() - group_b.mean()) / pooled_std if pooled_std > 0 else 0.0
        
        current_power: float = self.calculate_power(effect_size, len(group_a))
        required_n: int = self.calculate_sample_size(effect_size, target_power)
        
        logger.info(f"Calculated effect size: {effect_size:.4f}, Current power: {current_power:.4f}")
        return {
            'effect_size': float(effect_size)
            'current_power': float(current_power)
            'required_sample_size': required_n
            'target_power': target_power
            'alpha': self.alpha
        }

Pattern 3: BAD vs GOOD Implementation

# BAD: Hardcoded values, no validation, ignores statistical assumptions
def bad_power_calc(data):
    return 0.8  # Magic number, no calculation

# GOOD: Parameterized, validated, uses established statistical library
def good_power_calc(effect_size: float, n: int, alpha: float = 0.05) -> float:
    if n <= 0 or not (0 < alpha < 1):
        raise ValueError("Invalid parameters")
    from statsmodels.stats.power import TTestPower
    return float(TTestPower().power(effect_size=effect_size, nobs=n, alpha=alpha))

Best Practices

• ✅ Always validate your implementation on test data
• ✅ Document your assumptions and methodology
• ✅ Use version control for reproducibility
• ✅ Monitor performance metrics in production
• ✅ Periodically review and update your approach
• ✅ Test with edge cases and outliers
• ✅ Log all significant operations for debugging
• ✅ Follow the DRY (Don't Repeat Yourself) principle to avoid duplicating power calculation logic across projects

Common Pitfalls

| Pitfall | Problem | Solution | |

---

---

Constraints

MUST DO

• Validate all data preprocessing steps are fit-only on training data, never on validation or test sets
• Implement reproducible pipelines with fixed random seeds and deterministic operations where possible
• Report model performance with confidence intervals via bootstrapping or cross-validation across multiple runs
• Log all experiments with parameters, metrics, and artifacts using MLflow or equivalent tracking system

MUST NOT DO

• Do not evaluate a model on the same data used for training — always hold out a proper test set
• Avoid overfitting to the validation set by limiting hyperparameter search iterations
• Never use features that can only be computed at inference time (look-ahead bias)
• Do not report single-run accuracy without statistical significance testing or error bars

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• Statistical Power — Wikipedia
• Statsmodels Power Analysis
• G*Power Documentation (University of Düsseldorf)
• Sample Size Calculation (NIST Handbook)
• Power Analysis with Python (Statsmodels examples)