Random Selection Skill

Perform random selection with various algorithms for fair, unbiased outcomes.

When to Use

• Prize drawings
• Random sampling
• A/B test group assignment
• Survey participant selection

Core Capabilities

• Simple random selection
• Weighted random selection
• Stratified sampling
• Shuffle/randomize lists
• Unique selection (no duplicates)
• Reproducible randomness (seeded)

Examples

# Bash: Random line from file
shuf -n 1 items.txt

# Python: Simple random
import random
items = ['A', 'B', 'C', 'D']
selected = random.choice(items)

# Python: Multiple unique items
selected = random.sample(items, 2)

# Python: Weighted selection
weights = [10, 5, 3, 1]
selected = random.choices(items, weights=weights, k=1)

# Python: Seeded (reproducible)
random.seed(42)
selected = random.choice(items)

Algorithms

• Uniform: Equal probability
• Weighted: Based on weights
• Reservoir sampling: For streams
• Fisher-Yates shuffle: Unbiased shuffling

Best Practices

• Use cryptographically secure random for security
• Document seed for reproducibility
• Verify distribution for large samples
• Handle edge cases (empty list, single item)

Resources

• Python random: https://docs.python.org/3/library/random.html
• secrets (secure): https://docs.python.org/3/library/secrets.html