Normal Distribution
nothing is called 95% until it has been counted
Concept

Power — where it appears

The chance a test rejects when the effect it is looking for is really there, which is a property of a procedure at a stated alternative. A procedure that cannot reject at a true null cannot reject at a false one either, so a conservative test's validity and its uselessness are the same fact.

Named by 3 essays across 2 fields — each of them below, with the objects they name alongside it.

The distribution of the largest statistic in the table. Fit the benchmark to the whole series, resample its residuals, simulate 199 series in which the null is true by construction, re-run the entire eight-variant search on each, and keep the largest statistic. That is the distribution drawn here, and it is the distribution of the thing a specification search actually reports. It is centred at 1.045 — the maximum of eight statistics is not centred at zero however well each of them behaves — and its 5% point is 2.536. A table read against 1.671 is reading the distribution of one statistic; a Bonferroni correction reads it against 2.577 and is nearly right here, because eight variants that each add a different lag are nearly eight separate chances.

A null with a model in it

The distribution to read the winner of a table against cannot be resampled from the data, because the data does not contain the null. It has to be generated from a model — which is the assumption the resampling was chosen to avoid.

search · Bootstrap What each analysis does at a true null, by shape. Four analyses of the same trials — 500 of them at each shape, 120 units, assigned by the rule that reads the covariate. Every rejection is false. The unadjusted analysis is the one that moves: 1.60% against a linear outcome, where the design removed a great deal that the standard error still prices, and 5.20% against a quadratic, where it removed nothing and the standard error is right. Adjusting holds the level in all three columns, and so does the design's own reference distribution, which needs to be told the rule and nothing else.

The analysis and the shape

An unadjusted analysis after a rule that read the covariate is too cautious — by a third against a linear outcome, by nothing at all against a quadratic. And an adjustment for the wrong function recovers almost none of the precision the right one would.

shape · Randomisation Eight encompassing nulls, all of them true at once. The forecast under test is the variance-minimising combination of the eight candidates, and the first-order condition that defines its weights is cov(e_c, e_j) = var(e_c) for every j — so every one of the eight nulls is exactly true simultaneously and every rejection counted here is false. 800 draws of 60 origins. The largest of the eight statistics rejects 25.0% of the time at a nominal 5% and one comparison stated in advance rejects 3.1%, which makes the set worth about 9.1 independent comparisons — nearly the eight it has. Bonferroni, which is far inside its level on a search over accuracy comparisons of the same eight forecasters, is at 4.8% here.

When every null is true

A reality check assumes that every candidate in the set is exactly as good as the benchmark, which is a configuration nobody's data is ever in. Test a combination against its own parts and that configuration is not assumed — it is what the arithmetic makes true.

search · Multiplicity

Named alongside it

The objects these essays reach for when they reach for this one.

Reference distributionBlock bootstrapBonferroniError rateMultiplicityNull hypothesisAllocation ruleBenchmark forecastBootstrapClark–WestCombination weightConditional inference

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