The uncertainty around your measured edge, computed from the spread of trade results and the number of trades.
Standard error = standard deviation of R multiples / sqrt(n). If your trades have a standard deviation of 1.6R and you have 100 of them, the standard error of your mean is 0.16R.
Put that next to a measured expectancy-per-trade of plus 0.22R. Two standard errors is 0.32R, so the 95% interval runs from minus 0.10R to plus 0.54R - it includes zero. After 100 trades, a seemingly solid edge has not been demonstrated at all. To get the interval clear of zero you need roughly n > (2 x 1.6 / 0.22)^2 ≈ 212 trades, and that assumes the edge is stable throughout.
This single calculation resolves most arguments about whether a strategy is working. It also explains why outlier-dependent systems need enormous samples: their R standard deviation is large, so the numerator of the standard error stays high.
Original diagrams for the ideas on this page. Illustrative, not real market data.
Expectancy: the average trade. Forty trades sorted by outcome: 24 small losses and 16 larger wins. Weighting each side by how often it happens gives the average result per trade, marked here by the dashed line at +$120.
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