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name: ds-experimental-design compatibility: opencode completeness: 95 content-types:

• code
• guidance
• do-dont
• examples description: '"Provides Designs experiments using design of experiments (DOE), factorial designs, randomization, and blocking for efficient learning"' license: MIT maturity: stable metadata: domain: coding output-format: code related-skills: ds-ab-testing, ds-online-experiments, ds-randomized-experiments ds-statistical-power ds-statistical-power role: implementation scope: implementation triggers: experimental design, DOE, factorial design, randomization, blocking, how do I design experiments 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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Experimental Design

Comprehensive guide to experimental design 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 experimental design
• Implementing best practices for experimental design
• Optimizing model performance using experimental design techniques
• Learning industry-standard approaches to experimental design

When NOT to Use This Skill

• When using pre-built libraries without understanding underlying concepts
• For toy problems that don't require experimental design 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

Experimental Design 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 Experimental Design

import pandas as pd
import numpy as np
from scipy import stats

def generate_factorial_design(factor_levels: dict) -> pd.DataFrame:
    """Generate a full factorial design matrix from factor levels."""
    keys = list(factor_levels.keys())
    values = list(factor_levels.values())
    grid = np.meshgrid(*values, indexing='ij')
    design = pd.DataFrame(np.column_stack([g.ravel() for g in grid]), columns=keys)
    return design

def apply_randomization(design: pd.DataFrame, seed: int = 42) -> pd.DataFrame:
    """Randomize the order of experimental runs using a reproducible RNG."""
    rng = np.random.default_rng(seed)
    indices = rng.permutation(len(design))
    return design.iloc[indices].reset_index(drop=True)

def create_blocks(design: pd.DataFrame, n_blocks: int) -> pd.DataFrame:
    """Assign experimental runs to blocks to control for nuisance variables."""
    block_assignments = np.repeat(np.arange(n_blocks), len(design) // n_blocks)
    remainder = len(design) % n_blocks
    if remainder > 0:
        block_assignments = np.append(block_assignments, np.arange(remainder))
    design['block'] = block_assignments
    return design

Pattern 2: Production-Ready Experimental Design

import logging
from typing import Any, Dict, List
import pandas as pd
import numpy as np
from scipy import stats

logger = logging.getLogger(__name__)

class ExperimentalDesign:
    """Production implementation of Experimental Design following ISO 3534 standards."""
    
    def __init__(self, seed: int = 42):
        self.seed = seed
        self.rng = np.random.default_rng(seed)
        
    def execute(self, data: pd.DataFrame, response_col: str, factors: List[str], n_blocks: int = 1) -> Dict[str, Any]:
        """Execute Experimental Design on data with randomization and blocking."""
        if response_col not in data.columns:
            raise ValueError(f"Response column '{response_col}' not found in data")
        if not all(f in data.columns for f in factors):
            raise ValueError(f"All factors must be present in data: {factors}")
            
        design = data[factors].copy()
        design = apply_randomization(design, self.seed)
        design['block'] = np.repeat(np.arange(n_blocks), len(design) // n_blocks)
        remainder = len(design) % n_blocks
        if remainder > 0:
            design.loc[len(design) - remainder:, 'block'] = np.arange(remainder)
            
        design['response'] = data[response_col].values
        
        if n_blocks > 1:
            groups = [group['response'].values for _, group in design.groupby('block')]
            f_stat, p_val = stats.f_oneway(*groups)
        else:
            f_stat, p_val = np.nan, np.nan
            
        results = {
            'status': 'success'
            'design_matrix': design
            'statistics': {'f_statistic': float(f_stat), 'p_value': float(p_val), 'n_runs': len(design)}
            'metadata': {'factors': factors, 'blocks': n_blocks, 'seed': self.seed}
        }
        return results

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

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.

• Design of Experiments — Wikipedia
• NIST Engineering Statistics Handbook
• DOE Guide (Six Sigma)
• Randomized Block Design — StatLect
• Factorial Experiments (Khan Academy)