What is Sample Size Rule?
The sample size rule in trading is the principle that no meaningful conclusion should be drawn from the performance of a setup until it has at least 30 closed trades (for directional confidence) or 100 trades (for statistical robustness). Evaluating win rate or expectancy on fewer trades conflates luck with edge.
Indian market context (NSE)
Reference levels: Nifty 50 at 24,300, Reliance Industries at ₹1,300, Bank Nifty futures at 55,000 (lot size 30). Examples below show how Sample Size Rule shows up on Indian index, equity, and futures books — update to live quotes in your journal.
Nifty 50 perspective
Sample Size Rule in Indian context at Nifty 24,300: apply SEBI/regulatory framing where relevant and tag index trades separately in weekly review.
Reliance Industries perspective
Sample Size Rule using Reliance at ₹1,300 as a liquid large-cap example — adjust numbers to your live quote and contract note.
Bank Nifty futures perspective
Sample Size Rule with Bank Nifty futures at 55,000 — respect lot size 30 and quarterly vs monthly contract rules on NSE.
How to validate
- Validate Sample Size Rule with a written rule and at least 20 tagged examples.
- Ask whether the reading changed because of process or one outlier trade.
- Compare two independent time windows before adjusting position size.
- Document validation date in weekly review notes.
How to track in TradeLyser
- Mention Sample Size Rule in trade comments when it influenced the decision.
- Mirror the term in weekly review questions for consistency.
- Filter trades mentioning the concept during monthly analytics.
- Cross-link to related glossary terms in mentor notes.
Best practices
- Teach Sample Size Rule the same way to mentors and peers — shared vocabulary.
- Re-read this page after major rule changes to Sample Size Rule usage.
- Prefer one improvement per month over ten simultaneous tweaks.
- Link learn articles when Sample Size Rule needs deeper study.
Common pitfalls
- Using Sample Size Rule buzzwords without measurable journal tags.
- Copying another trader’s Sample Size Rule rule without sample size context.
- Skipping weekly review because the term feels “basic”.
- Letting social media redefine Sample Size Rule mid-quarter.
Reference guide
| Context | Value | Reading |
|---|---|---|
| Minimum threshold | 30+ trades before evaluating a setup; 100+ before scaling | Judging a setup as broken after 3–5 losing trades |
Related terms
An edge audit is a structured review — typically monthly or quarterly — in which a trader examines the expectancy, win rate, and profit factor for each setup tag and strategy in their journal to confirm that a statistical edge is present, stable, or improving. It also identifies setups that have degraded and should be paused.
Expectancy answers whether your edge pays each time you repeat the setup. Positive expectancy means the system earns over many trades; negative expectancy means it bleeds even with a high win rate.
A funded account provides trader capital after passing evaluation, with profit split and risk limits.
Intraday trading opens and closes positions within the regular session without overnight hold.
A strategy scorecard is a one-page summary of the core performance metrics for a specific trading strategy — including win rate, expectancy, profit factor, max drawdown, average R-multiple, and sample count. It enables side-by-side comparison of multiple strategies and quick identification of which deserve more capital allocation.
By trader level
Level up — system optimisation
Already journaling? Use these metrics to measure your edge, manage risk, and evolve your system.
FAQ
How does TradeLyser handle small sample sizes?
TradeLyser displays a data-reliability indicator next to all statistics. When sample count is below 30, it shows a "Low sample" warning to prevent over-interpreting noisy data. When you hover over any metric, it shows the underlying trade count.
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