Sample Size — How Many Trades Before Your Data Means Something.
Part of our masterclass path. We systematically cover risk, logic, and mechanics to build professional edge.
What is covered in this chapter
The Mathematics of Sample Sizes
A common rookie mistake is backtesting 20 trades, seeing a 70% win rate, and assuming they have found an edge. Under probability theory, a 20-trade sample size is statistically meaningless; it is noise.
The Laws of Large Numbers
To achieve statistical validity, you need a minimum sample size of 100 to 200 trades, spanning at least 12 months of market data. This ensures your strategy is tested across different market environments: uptrends, downtrends, consolidations, and high-impact news weeks.
We explain how variance behaves across different sample sizes, proving why a larger dataset protects you from choosing a failing strategy.
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