This is the Electi review. Today, random guessing is expensive, and most small and mid-sized businesses running Monte Carlo simulations for forecasting or risk management are still paying for it. Alecti's guide argues that plain random sampling wastes computing time and money by unevenly covering the range of possible outcomes. Some regions get hit repeatedly, others barely at all. The fix it proposes is Latin hypercube sampling, a method that divides every input variable into equal strata and guarantees exactly one sample per stratum. The result? Full, even coverage of the possible outcome space using a fraction of the samples random methods require. The guide walks through the mechanics without jargon. It shows how LHS works compared to standard Monte Carlo, provides working Python code so a business can implement it directly, and applies it to real scenarios, demand forecasting, financial risk modeling, and operational optimization. The pitch to SMBs is efficiency. They don't have enterprise compute budgets, so a method that reaches statistical reliability with fewer runs is not a nice to have. It's the difference between affordable modeling and none at all. Here's the skeptical read. The article is educational content from a consulting style provider, and the simplicity of just use this instead glosses over real trade-offs. LH assumes you can correctly define your input distributions and their independence, assumptions many SMBs will get wrong without statistical expertise. Structure only helps if the structure reflects reality. A neatly stratified grid built on bad assumptions is still bad forecasting, just faster and more confident looking. Still, the core claim holds up on the math. For a fixed sample budget, stratified sampling reduces variance versus pure randomness. That's not controversial in statistics. What's being sold here is accessibility, code and explanation aimed at businesses that would otherwise default to cruder tools or gut instinct. The real test isn't the technique, it's whether the business feeding it knows its own inputs well enough to trust the output. That's the review.