What t-statistic should a strategy have?

A value comfortably above 2 on a hypothesis defined before the test is the usual minimum for taking a result seriously. After a search over many configurations, a much higher bar or an explicit correction for the number of trials is needed.

Is a t-statistic the same as a p-value?

No, but they are linked: the p-value is the probability, under the null hypothesis of no edge, of a t at least as extreme as the one observed. The t is the distance; the p-value is the tail probability of that distance.

t-statistic of trade results

The t-statistic of a set of trade results is the average trade divided by its standard error, the standard deviation over the square root of the number of trades. It measures how many standard errors the average lies from zero; values far from zero are harder to attribute to chance, under the assumption of independent trades.

Senzoukria · Glossary · Updated September 2026

At a glance

Formula: t = mean ÷ (s ÷ √n), s = sample standard deviation
Rough 5% threshold: |t| ≈ 1.96 on large samples, two-sided
Link to SQN: SQN with population σ = t × √[n ÷ (n − 1)]
Weakened by: Dependence, fat tails, multiple testing

Formula and examples

Compute the mean of the trade results, their sample standard deviation s, and divide the mean by s ÷ √n. Sixty-four trades with a mean of +20 dollars and a standard deviation of 160 give t = 20 ÷ (160 ÷ 8) = 1.0: the average is one standard error above zero, which chance produces easily. Four hundred trades with the same mean and dispersion give t = 20 ÷ (160 ÷ 20) = 2.5. The strategy has not improved; the evidence has.

With many observations the t distribution is close to the normal distribution, so |t| above about 1.96 corresponds to a two-sided p-value below 5% under the model. With few trades, the threshold is higher.

Relationship with the SQN and the Sharpe ratio

A per-trade Sharpe ratio is mean ÷ standard deviation; multiplying it by √n gives the t-statistic. Van Tharp's system quality number uses the same construction, √n × mean ÷ standard deviation. Computed with the population standard deviation, as in the Replay statistics of Senzoukria, the SQN equals the t-statistic multiplied by √[n ÷ (n − 1)], a factor that is 1.005 at 100 trades. Reading an SQN is therefore reading a t-statistic, with the same caveats.

Why the number overstates certainty

Trades are not independent when they cluster by regime or by session, which makes the true standard error larger than s ÷ √n.
Fat tails make the standard deviation itself unstable on small samples.
A t of 2.5 for the best of fifty configurations is not a t of 2.5 for a hypothesis stated in advance; the selection must be accounted for, for instance with a deflated Sharpe ratio.

In Senzoukria

The Replay report and the Performance panel give a verdict line built on the sample size and the SQN: under 20 trades the ratios are shown as exact but not significant; with a computable SQN, below 0 means no edge in the sample, below 1.6 weak, below 2.5 average, and 2.5 or more 'solid on this sample'. An SQN of 5 or more on fewer than 100 trades is flagged as suspicious, because a few trades are then carrying the result. The Gauntlet adds a probabilistic Sharpe ratio, a deflated Sharpe and a permutation p-value, which relax the normality and selection assumptions a plain t-statistic makes.

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Frequently asked questions

What t-statistic should a strategy have?: A value comfortably above 2 on a hypothesis defined before the test is the usual minimum for taking a result seriously. After a search over many configurations, a much higher bar or an explicit correction for the number of trials is needed.
Is a t-statistic the same as a p-value?: No, but they are linked: the p-value is the probability, under the null hypothesis of no edge, of a t at least as extreme as the one observed. The t is the distance; the p-value is the tail probability of that distance.

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