Normal Distribution
nothing is called 95% until it has been counted
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The thread: Which rate is being controlled

Almost every procedure in this subject promises to hold some quantity at 5%, and they are not the same quantity. A familywise rate, a false discovery rate, a coverage, a per-look error rate: each is a different promise, and a method that keeps one can break another by a wide margin without doing anything wrong.
The number the comparison was missing. What it costs to choose the tuning parameter for every candidate separately rather than once for the table, under AR(1) at 0.8, paired on the draw. The window's figure is the one the earlier field reported; the order's is the one it named and did not make. They are the same size — 0.00401 against 0.00360, at 2.30 and 1.72 paired standard errors — and matching the lists at eight values leaves them the same size again. The prediction that the longer list would make the order's cost the larger of the two is not what happens; what happens is that the two rules cost the same once they are scored by the same criterion, which took a missing term to arrange. How long the list is

The comparison that was not made

Choosing a whitening's window separately for every candidate costs 0.00401 of regret. The same question about an order was named and left, because the two lists are different lengths. The order's answer is 0.00360, and matching the lists changes almost nothing.

 The end of the family is the member the algorithm cannot reach. Every member of the Φₚ family optimised on the same 11² candidates, and two eigenvalues of each answer. The upper curve is the smallest eigenvalue of the information matrix — the quantity E-optimality maximises — which rises from 0.0993 at the D end to 0.1994 at p = 64. The lower curve is the gap between that eigenvalue and the next one up, which falls from 0.0611 to 0.0011. A smallest eigenvalue is not differentiable where it is repeated, and the family is driving the gap to zero: the one criterion here whose meaning fits in a sentence is the one whose optimum sits on a corner of its own surface. The candidate grid is on a slider and it answers a narrower question than it looks. On this square region, refining an odd grid from seven to eleven moves nothing at all — the optimum's support is the corners, the edge midpoints and the centre, and every odd grid from five up contains all of them. An even grid has no centre point and cannot reach the answer at any member of the family. On a disc, where the boundary passes through no grid point, refinement does move it. The criterion, and what it assumes

The family behind the letters

A, D and E are not three ideas. They are three points of one family with a single dial, and running the dial from one end to the other doubles the smallest eigenvalue of the information matrix while closing the gap above it fifty-three-fold — which is the family driving its own last member to the place where it stops being differentiable.

 The familywise error rate with no correction, α = 0.05. Two routes: the curve is 1 − (1 − α)^m and the points are counted over 6,000 simulated families of true nulls. With twenty tests the chance of at least one false positive is 64.1%. Corrections, and what each controls

What the correction corrects

Twenty tests of true nulls produce at least one false positive 64% of the time, and the closed form and the count agree. Bonferroni holds it at 5% and Holm holds it at 5% while finding more. Nobody should still be using Bonferroni.

 One true null, one table, five readings. every subset of four, fifteen models, at a null where nothing any candidate holds is worth anything, over 500 draws. Each bar is the share of draws on which that reading declares a difference at a nominal 5%. The reading is the whole of the difference between the bars: the data is identical. An open search over all 210 ordered pairs rejects 76.2%; the table's own 5% point is 3.163 against the 1.671 a single comparison uses. Bonferroni takes the open reading to 0.6% — and on the nested sequence the same correction does not reach the nominal level at all, because there the excess is a shift in the mean rather than a maximum over many. A search with no fixed point

When the benchmark is a candidate

A specification search with a benchmark nailed down is the case with a closed form. Take the nail out — let the model that would have been reported be one of sixteen, chosen by the same data as its rivals — and the same true null is read three ways, at 2.0%, 7.8% and 76.2%.

 Testing at 0.05 every time the data is looked at. The null is true in every one of these trials and the test is correct every time it is run. Looking once rejects 4.9% of the time, as it should; looking ten times rejects 19.2% of the time. Nothing changed except permission to look. Stopping rules

When the looking happens

A p-value is defined relative to a sampling plan, so the same data means different things under different stopping rules. Testing five times at the nominal level rejects a true null 14% of the time, and no observation in the dataset changed.

 What 20 analyses of one dataset are worth. The threshold giving a 5% family-wise error rate, read back as a number of independent analyses. At no correlation it is 20.05; at 0.6 it is 11.37; at 0.95 it is 2.58. Bonferroni divides by 20 throughout. The analyses that were available and not run

How many analyses there really were

Bonferroni divides by twenty because twenty analyses were run. Twenty analyses of one dataset are worth 11.37 independent ones at a correlation of 0.6 and 2.58 at 0.95, and the threshold that controls exactly the same error rate is measurable rather than assumed.

 Two 95% bands for a quantile plot of 40 points. The outer band is left by 5% of genuinely normal samples — which is what a reader is using a band for. The inner one holds each point separately at 95%, which is what software draws, and 45.0% of genuinely normal samples step outside it. The outer is the inner widened by a factor of 1.502. What a diagnostic plot is showing

The band the eye was standing in for

The confidence band software draws on a quantile plot holds each point at 95%, and a genuinely normal sample of forty has forty chances to leave it — so 45.0% of them do. The band a reader is actually using is that one widened by a factor of 1.502, and nothing draws 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. Searching among fitted models

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.

 Which window is better depends on who chose the block length. The margin between a rectangular block and a tapered one, on 400 samples of 120 rows, under four rules for choosing the block length. At the length that would actually have been best on each draw the taper is ahead by 2.12 points of a 35.8% error, at 22.1 paired standard errors; at a length estimated from the sample's own persistence it is ahead by 1.89. At the length this field's own figures use — eight — the rectangle is ahead by 2.04, and at the rule of thumb by 4.54. Every rule sees the same draws. What separates them is the length: the two rules that lose to the rectangle pick 4.00 and 8.00 where the best available is 24.57, and a tapered window at a quarter of the right length has thrown away most of what it was weighting. A block length chosen from the data

An ordering that depends on the rule

The tapered block beats the rectangular one at the best available block length and at one estimated from the data. At a length written into a protocol, and at the rule of thumb, the rectangle wins — at every sample size measured.

 Eight candidates, one of them exactly as good as the benchmark. The candidate set: moving averages of the last 1, 2, 3, 5, 8, 13, 21 and 34 observations, each drawn as its expected squared error divided by the benchmark's — the mean of all 60. The persistence is not chosen, it is solved for: at φ = 0.4895 the best candidate in the set, the average of 2, has exactly the benchmark's expected squared error, and every other candidate is worse by between 0.5% and 5.1%. So the null that no candidate beats the benchmark is true, with one candidate on its boundary. Everything a set comparison claims about its own error rate has to be measured here, because anywhere further inside the null every procedure flatters itself. The best of a set, and what the search costs

Eight forecasters and one benchmark

A set of forecasters is a multiplicity problem on top of a dependence problem, and the two do not separate. Eight windows of one series carry the multiplicity of two and a half independent comparisons; eight separate problems carry eight.

 Four boundaries for 5 looks, all spending 5% in total. test at 0.05 every look: 1.96, 1.96, 1.96, 1.96, 1.96. Pocock — a constant, higher boundary: 2.41, 2.41, 2.41, 2.41, 2.41. O'Brien–Fleming — strict early, nearly nominal at the end: 4.55, 3.22, 2.63, 2.27, 2.03. Bonferroni across looks: 2.58, 2.58, 2.58, 2.58, 2.58. Every one except the first spends the same total error rate; they differ in when they spend it. Stopping rules

Spending the error rate

The repair for interim testing is to spend 5% across the looks rather than at each one. The boundaries are solvable rather than quotable, and a trial that can stop early uses 298 observations where a fixed design uses 400 — at a cost of half a point of power.

 Three charges, and only one of them is a test. What each of three thresholds does to the same decision, under AR(1) at 0.8, against the size of a genuine break in the mean at row 60. A chi-square on the 5 coefficients a split adds — 11.07 — declares a break on 73.6% of samples that have none: it is not a test at all. The break search's own 95% point, 69.6, carried into a rule that also chooses its window, fires on 0.0% of null samples and on 0.0% of samples with the largest break measured — the natural way of combining two published corrections does not lose a little power, it switches the test off. The calibrated charge, 27.2, holds 5.6% at no break and reaches 29.2% at the largest. Two searches over one sample

The charge that is not a sum

Charging two searches what each costs on its own is conservative, and conservative here means the test never fires. At the largest break measured it declares nothing, on every draw, while a calibrated threshold reaches 29%.

 The mean's zero is the copula's symmetry. Five copulas, each at a Spearman rank correlation of 0.4, with a normal covariate throughout — so nothing here is about the marginal, which is the whole of the earlier field. Horizontally: how far the copula's density is from its own reflection through the centre of the unit square, measured rather than read off the family's name. Vertically: what a rule balancing the mean of each covariate removes of their product. The three copulas at zero on the horizontal axis remove exactly nothing, to thirty decimal places. The two that are not symmetric remove 7.71%. A guarantee that held for six marginals turns out to have needed something the marginals could not have told anybody about. The other half of the dependence

The symmetry the marginals could not show

A mean's interaction zero needs the covariate to be symmetric and the copula to be symmetric under reflection. Six marginals could only ever test one of those, and the other is broken by the commonest kind of dependence there is.

 Where a Ds-optimal design puts its runs. The Ds-optimal measure over 121 candidate settings on a square region. It keeps 9 of them and discards the rest, and the 9 it keeps are the settings a catalogue would have offered without any of this arithmetic. What the search adds is the weights: 0.2500, 0.1250, 0.0625, which nine equal runs cannot express. The criterion, and what it assumes

The two terms anybody wanted

D-optimality estimates all six parameters of a quadratic as precisely as possible. Nobody wants that. An experimenter looking for a maximum wants the two curvature terms, and the design that gives them is not the D-optimal one — it is a quarter of the runs at the centre, exactly, and the D-optimal design is 75.3% efficient for the question that was actually asked.

 The term that cancels, and the term that does not. The volume each candidate's whitening moves — log|Ω̂| — for a sieve of order 4 on one sample of 120 rows. Estimated once from the fullest candidate and used for the whole table, it is the same number for every candidate, so it drops out of every difference the criterion reads: that is why nothing in this collection has ever needed to carry it. Estimated from each candidate's own residuals it ranges over 23.87, which is more than a parameter is worth, and the criteria being compared are then fits made under different error models with no term saying so. The window's rule has carried this term since the estimated-covariance field and the sieve's never had it. How long the list is

The volume a whitening moves

A sieve's whitening has a determinant and this collection's criterion for it never carried one. Shared across a table the term cancels exactly, which is why nothing ever noticed; used per candidate it is worth more than a parameter and the whole comparison turns on it.

 Twenty tests, 10 of them real — what each procedure holds. no correction: familywise 40.8%, false discovery 5.3%, power 85%. Bonferroni: familywise 2.8%, false discovery 0.5%, power 49%. Holm: familywise 3.6%, false discovery 0.6%, power 53%. Benjamini–Hochberg: familywise 20.0%, false discovery 2.6%, power 75%. Corrections, and what each controls

Two different promises

Bonferroni bounds the chance of any false positive. Benjamini–Hochberg bounds the share of the findings that are false. Both are called correcting for multiple comparisons, and one of them lets the familywise rate reach 20%.

 What a 95% credible interval covers, n = 20. Computed by summing over all 21 possible counts rather than by simulating them. Jeffreys' prior covers close to 95% across the range; a confident prior centred in the wrong place covers almost nothing where the truth is far from it. The prior, doing visible work

What a credible interval covers

A credible interval makes the statement everyone wants and does not claim to have a coverage. It has one anyway, it can be summed over the sample space exactly, and on a reasonable prior it beats the interval taught first.

 The pooled two-sample test's size, with a true null everywhere. Forty units split between two groups, with the second group's variance a stated multiple of the first's, and the two population means equal. A 5% test should reject 5% of the time. The pooled test runs from 0.55% to 18.91% across this region; Welch's runs from 4.63% to 5.51%. Shape, and what it does to a two-sample test

A degrees of freedom that is not a count

The pooled two-sample test's size runs from 0.55% to 18.91% across forty units split five ways against five variance ratios, with a true null in every cell. Welch's runs from 4.63% to 5.51% — bought with a degrees of freedom that is a function of the data, not an integer, and not a count of anything.

 The best of 8 arms, tested as though it were the only one. 8,000 trials with no effect in any arm. Stage one runs 8 arms at 60 each, the best is carried forward, and stage two adds 30 more to it and to the control. The histogram is where the final statistic lands and the curve is the standard normal it is being read against — shifted right, because the arm was chosen for being ahead. 10.5% of these trials clear 1.96 against a claimed 5%, and the value that actually holds the rate for this design is 2.313. Designs that change while they run

Dropping the losers

Carrying the best of eight arms forward and testing it at 1.96 rejects a true null 10.3% of the time — the hypothesis was chosen by looking at the data, so the statistic is a maximum wearing a single comparison's clothes. The value that holds the rate is 2.313, and it has to be solved for.

 Four criteria on 5 designs, on a square region. Each design scored as an efficiency — its value over the best attainable — so four criteria in as many different units sit on one scale where 1 is the optimum. Rows are ordered by D. D picks 13-run exchange; A picks face-centred composite; G picks 13-run exchange; I picks face-centred composite. Every design has been scaled to just fit the region first, because a design run at settings the region does not contain is not a competitor on it. The disagreement is the point: the letter is a choice, and it is almost never reported as one. A design chosen rather than looked up

Four letters and two camps

D, A, G and I are four ways of turning one matrix into one number, and they do not agree. The design that wins on D is the worst thing here on I. And the same two designs swap places entirely when the region changes from a square to a disc — on all four criteria at once.

 A wider band is always a better fit. The likelihood maximised over the band, at five widths, averaged over 30 samples. A band at L lags is a band at L + 1 with the last entry held at zero, so the families are nested and the maximised likelihood cannot fall — it does not, on any draw. What it does is rise at 0.984 of log-likelihood a lag. A parameter that is doing nothing buys half a unit in expectation and Akaike's criterion charges one, so this is a criterion very nearly indifferent between every width on offer. The dashed line is what a charge of one unit a lag would exactly cancel. Nothing in the fit chooses a width, and what does choose one is a charge somebody has to pick. A covariance with no parameter

Nothing in the fit picks the width

A wider band is always a better fit, and it is better by about one unit of log-likelihood a lag — which is the order of what a criterion charges for a parameter. Three defensible rules choose widths a factor of three apart.

 3 arms against one control, 360 units in all. Every control size, enumerated. The best is 132 on the control and 76 on each arm — a ratio of 1.74, against √3 = 1.73. Splitting the units evenly over all 4 groups costs 7.2%, which is small; what the larger control also does is lower the correlation between the comparisons, from 0.50 to 0.37, and that changes which multiplicity correction is right. Splitting the units

One control, many arms

The control appears in every comparison, so it is worth √k treatment arms — and the same sharing makes the k tests correlated at n/(n+n₀), which is the quantity Bonferroni ignores. Both facts come out of one design decision, and it is the size of the control.

 Four analyses of the same trials, with no treatment effect at all. 700 trials of 120 patients allocated by minimisation at p = 0.8, with the prognostic factors carrying a real effect on the outcome and no treatment effect — every rejection below is a false one. Two statistics, the plain difference and the same after adjusting for the balanced factors, each read against two reference distributions: a t table, and the set of allocations the rule could have produced from these covariates. The unadjusted comparison rejects 0.6% where it claims 5% — conservative, which is a loss of power rather than an error, and nothing on the output says so. Adjusting puts it back at 5.4%. Both re-randomised versions are at their nominal level by construction, whatever statistic goes into them. Balancing on what was recorded first

The analysis has to know the rule

A trial balanced by minimisation and analysed by comparing the two arms' means rejects a true null 0.6% of the time where it claims 5%, and at full determinism 0.0%. That is not an error anybody complains about — it is a test that has stopped working, paid for by a balance the analysis then refused to use.

 Sixteen candidates nobody would have run, and what they cost. At φ = 0.65 one candidate in the original set is genuinely better than the benchmark, and the question is how often each procedure finds it. The added candidates are stale copies of the last value — read two, four, six … steps late — every one of them worse than the benchmark by at least 43%, and not one of them is ever the best candidate in a sample. The reality check goes from 35.5% to 0.0% as they are added, because its reference distribution has to assume every candidate is exactly as good as the benchmark and sixteen such assumptions is a critical value nothing reaches. The recentred version, which drops from the recentring the candidates the data has already ruled out — 15.1 of 24 of them — goes from 25.5% to 24.5%. The top line never moves: reporting the winner's own p-value cannot notice a change to a set it never looks at. The best of a set, and what the search costs

The models that were never in the running

A reference distribution for a set has to assume something about every candidate in it. Assuming that all of them are as good as the benchmark is what makes the reality check honest, and it is what sixteen hopeless candidates use to destroy it.

 Power to find a real effect of 3 standard errors, 10 of 20 real. no correction finds 85.1%, Bonferroni finds 49.1%, Holm finds 52.5%, Benjamini–Hochberg finds 74.9%. The uncorrected procedure finds the most and controls nothing. Corrections, and what each controls

The price of control

Every correction is paid for in power, and the exchange rate can be measured. Holm buys familywise control for 33 percentage points of power; Benjamini–Hochberg buys a weaker guarantee for 10. Neither is free and neither is a matter of taste.

 Exact coverage, at every block size. Coverage of the interval each rule reports, at a nominal 95%, over 2,500 runs each with a standard error of 0.44 points. The blinded rule stops on the within-block contrasts and reports an interval built from the block means, and those two are independent whatever the rule does — so the interval is an ordinary t interval on b − 1 degrees of freedom and its coverage is exact. It is exact at every block size drawn. The interval a practitioner writes at the purely sequential rule's stopping time covers 91.72%, and Stein's two-stage rule is exact for the same reason as the blinded rule and spends 2.10 times the observations to be so. The bars are truncated at 86% so the differences can be seen. What the procedure may not read

The rule that cannot see the mean

A sequential rule stops when its own estimate of the spread is small, which is more often on the samples whose spread came out low — so the interval afterwards is short. There is a way to keep updating the estimate and stop being able to see the mean at all.

 Each repair is for its own defect, and one is for both. The 95% point of the statistic's own distribution in each world, against the mean 95% point of five reference distributions built from one sample. Where the error variance is a function of the design, the two resamplings that detach a residual from its row fall short and the two multipliers that keep it there do not; where the rows repeat each other it is the other way round. With both defects at once the blocked multiplier — drawn once per run of 5 rows, so the residual never moves and its neighbours share a sign — is the closest of the five, at 2.999 against a truth of 3.803. It is still short by 0.804, and that shortfall is the next figure. Scoring a search without spending data

Two defects and one resampling

Four resamplings, each the repair for one defect and wrong about the other. Put both defects in the same world and the statistic's 5% point is 3.8028, where the best of the four reaches 2.8326 — until a multiplier that stays on its own row and shares a sign with its neighbours reaches 2.9988.

 The argument is a third of the size of the thing it is inside. Three quantities on one scale, in points of the error in a block resample's implied long-run variance, at 120 rows. The gap between the two windows at the best available block length — the whole subject of the comparison this field inherited — is 2.12 points. What the best rule a practitioner could actually run gives up against that same best length is 7.26, a factor of 3.42. What the rule of thumb gives up is 26.01. So the ordering between windows is worth establishing and is not worth arguing about, and the sentence that follows from it is not use the taper but estimate the block length, because that is where the points are. A block length chosen from the data

What choosing the length costs

The gap between two block windows at the best available length is 2.12 points. What the best rule a practitioner could run gives up against that same length is 7.26. The argument is a third of the size of the thing it is inside.

 Three analyses of the same trials, none of them wrong about the data. 320 trials at n = 60 with no treatment effect at all, so every rejection counted is a false one, and a covariate that drives the outcome with coefficient 1. The unadjusted comparison is at 5.94% after a coin — its level — and at 0.00% after the rule that reads the covariate: the design removed the imbalance and the analysis is still pricing it. Adjusting for the covariate gives 4.06%, and the rule's own reference distribution — hold the outcomes, re-run the rule 199 times, count — gives 3.13% against the 4.5% that 199 draws can deliver. The last of the three has to be told the assignment rule and nothing else, which is the one thing the experimenter certainly knows. Balancing what has no levels

What the balanced trial is worth

A rule that reads the covariate removes three quarters of the imbalance. An analysis that does not know it happened prices the imbalance anyway, rejects one true null in two hundred instead of one in twenty, and finds a real effect less often than a coin-tossed trial does.

 Which tail the threshold is in. What a rule balancing a threshold at 1 on each covariate's own scale removes of the interaction between the two thresholds, on five copulas matched at a Spearman rank correlation of 0.4 with a normal covariate throughout. This rule never had a zero to lose — the earlier field establishes that under every marginal — so what is left is a size, and the size depends on where the dependence lives. A Clayton copula, whose density piles up in the lower tail, leaves 5.33%; the same copula turned over, so that it piles up in the upper tail where the threshold is, leaves 33.36%. Same rank correlation, same Kendall tau, same marginal, same threshold: 6.26 times the leak, decided by which end of the distribution the dependence and the cut are both in. The other half of the dependence

Which tail the cut sits in

The same copula and its reflection have the same rank correlation, the same Kendall tau and the same marginals. A balancing rule holding a threshold at a dose leaves 5.33% under one and 33.36% under the other.

 One promise, kept on average and inside neither group. What each calibration scheme covers inside each of two equally common groups whose noise scales are 1 and 3, over 6000 draws with 200 calibration points. One interval for everybody covers 100.00% of the quiet group and 90.66% of the noisy one, averaging to 95.28% — and the closed form for that population says 99.9999% and 90.0001% at a half-width of 4.9346, from two normal cdfs and no simulation. Dividing by an estimated per-group scale gives 95.29% and 95.48%; calibrating separately inside each group gives 95.49% and 96.01% against a closed-form expectation of 95.4645%. Only the last of those is a guarantee rather than a repair, because the rank argument runs inside each group. Coverage without a distribution

Marginal is not conditional

One exactly valid interval covers 100.00% of a quiet group and 90.66% of a noisy one, and the floor is arithmetic rather than a measurement — a group of share π is guaranteed only 1 − α/π, which is zero when the group is as rare as the miss rate.

 Robust, at the sample sizes it is reached for. Counted coverage of five 95% intervals for a slope, at six sample sizes, under an error variance leaning towards the edges of the design (γ = 0.8), over 20000 draws at the small end. The model-based interval sits at about 87.06% everywhere and does not improve with the sample, because it is a claim about a variance it is not estimating. The robust ones do improve: HC0 covers 88.73% at 20 rows, 92.70% at 50 and 94.93% at 1,000. Its promise is asymptotic and its use is not, and the gap between those two facts is this picture. The leave-one-out correction read against a t on n − 2 is the only line that is near its promise at the small end: 94.55% at 20 rows. A standard error for a model that is wrong

Robust is not free

A robust standard error's promise is asymptotic and its use is not. Its 95% interval covers 88.73% at twenty rows, and under mild heteroskedasticity it is the worse of the two intervals until a hundred.

 Twenty cells of an interval that is exactly 95%, 1,000 replications each. The t interval covers exactly 95% in every cell. Estimated at 1,000 replications its cells read 93.9% to 96.5%, and 2 of the twenty are flagged by their own ±1.96 standard errors. What makes it checkable

A coverage table with its own error

Twenty cells estimating the coverage of an interval that is exactly 95%, at a thousand replications each, read from 93.9% to 96.5% — and a table like that flags at least one of its correct cells on 69.9% of honest runs. Ten times the replications does not repair it: at ten thousand the same table still flags one 63.3% of the time.

 The interval that over-covers when the instrument fails. Counted coverage of two nominal 95.0% intervals for the same causal effect, read off the same 2000 draws of 200 rows at each first stage. The exact Anderson–Rubin set covers 95.3% at every setting — flat, because the statistic it inverts is built from y − tβ, which contains no π at all, and is therefore the same number on the same draw whatever the instrument is worth. The conventional interval covers 99.1% at π = 0.02 and 95.6% at π = 0.6: it goes wrong at the weak end by covering too MUCH, at a median width of 7.320, because its standard error is computed from residuals taken at an estimate that has itself gone wrong. A weak instrument does not make this interval lie about its coverage; it makes it useless while telling the truth. A variable that moves one thing only

What the first stage does not know

A single weak instrument does not make the conventional interval undercover — it makes it cover 99.1% at a width of 7.320. Where the promise actually breaks is many instruments — coverage falls from 97.2% to 51.5% while the median width falls from 1.454 to 0.583.

 A bias against a variance, with the answer in between. How wrong one sample's reference distribution is, split into the two things it is wrong by. Sharing the multiplier over more rows keeps more of the dependence and closes the bias from 1.688 to 0.835; every row it is shared over also removes an independent sign from the 101 the sample started with, and the spread of the resulting quantile rises from 1.307 to 2.172. The distance a practitioner with one sample is actually exposed to is the two together, and it is smallest at ℓ = 5. Scoring a search without spending data

How long a block a multiplier shares

Sharing a sign over more rows keeps more of the dependence and leaves fewer independent signs to build a distribution from. The bias falls from 1.6885 to 0.8479 and the spread rises from 1.3073 to 2.1716, and the rejection rate walks straight through its nominal level on the way from 11.3% to 1.3%.

 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 shape the covariate enters by

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.

 What the corner costs when the table is full of hopeless candidates. The benchmark holds two predictors, one of which is worth 1; a third predictor, worth the amount on the horizontal axis, is held only by candidates the benchmark does not contain. At the left the null is true and both procedures hold their level. To the right there is a genuinely better candidate, and the uncorrected reality check finds it 2.7% of the time while the same test with the clearly bad columns recentred finds it 51.0% of the time. The columns doing the damage are the ones nobody would have looked at twice: they are so far behind that they cannot win, and calibrating as though they might is what makes the test blind. A search with no fixed point

The corner the test is calibrated at

"No candidate is better than the benchmark" is not a null but a face of a region, and a reality check is calibrated at one corner of it. Fill the table with candidates that are hopeless rather than equal and the test finds a genuine improvement 0.0% of the time.

 The rate falls geometrically; the count does not fall at all. The share of equal splits of two hundred units that a tolerance of 1 coin-spreads admits, against the number of functions the tolerance is stated for, with the closed form (2Φ(1) − 1)^k drawn beside it. The rate falls by about two thirds with every constraint. The admissible count is that rate times C(200, 100), and it goes from 2^195 to 2^192 — it does not fall in any sense a trial cares about. What the falling rate costs is sampling: 9,878 draws to collect a thousand admissible ones at six constraints, against 1,465 at one. When the set is too large to walk

What a reference distribution costs to sample

A randomisation test on a trial too large to enumerate has to sample its reference distribution, at 1/p attempts per draw and a p-value resolved to 1/(B + 1). Six constraints cost 9,878 attempts per thousand draws, and a thousand draws resolve p to 9.99·10⁻⁴ and not one digit finer.

 Free until the sums stop seeing what the differences see. Coverage with and without the block sums pooled into the interval's variance estimate. With one effect and one level they are free. With an effect that varies between blocks they are still free, because a block's sum picks that variation up exactly as its difference does. With a level that varies they make the interval 37% wider and conservative. And where the effect falls as the level rises — a ceiling, and not an exotic thing to suppose — the sums carry none of the between-block variation while the differences carry all of it, the pooled estimate is short, and the interval that uses it covers 88.75% on a width 20% narrower than the honest one. A promise about two arms

What a two-arm rule may not pool

A spread computed "within the block" without the arm label carries a share of the effect, so the trial runs 173 observations at a null and 282 at an effect of 1.5. The stopping rule is reading the thing it exists to measure, and the phrase that produced it is one word long.

 Fitting them together is worth something under one law. Four fits of the same regression under four dependences: least squares, the two-step plug-in every whitened rule in this collection runs, the coefficients and the band maximised together, and a whitening at the law's own covariance that nobody has. Under the moving average — the one law the band family contains — the joint fit beats the two-step by 0.0077 at 3.3 paired standard errors. Under the autoregression, long memory and the break it is a tie: 0.4, 1.0, 0.3 standard errors. That is the same ordering the likelihood gap gave, arrived at through the coefficients rather than through the objective. A covariance with no parameter

What fitting them together buys

Maximising over the coefficients and the covariance together beats the two-step under one of four dependences and ties under the other three. It is the one the band family contains, and the likelihood said so before any coefficient was compared.

 What the exactness costs, and the dial it is bought with. The median half-width of the interval each rule reports, at a requirement of 0.4 and a first look after 5 observations. The flat line is the interval a practitioner writes at the purely sequential rule's stopping time, which covers 91.72% rather than 95%. The curve is the blinded rule, which covers its nominal level at every block size: it reads b − 1 degrees of freedom where the other reads n − 1, and pays for the exactness in width. The best block size is 3, at 0.4712. Larger blocks give the stopping rule a better estimate and the interval a worse one, and the two costs go opposite ways, which is what puts the minimum in the middle. What the procedure may not read

What the blindfold costs

The exactly-covering rule pays for it in the width of the interval, and the block size is a dial between two costs that run in opposite directions. And on an interval whose width was fixed in advance, the same repair buys nothing at all.

 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. Searching among fitted models

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.

 Ordered stagewise: the outcomes at least as extreme as stopping at 160 observations with z = 3.3. Each column is one look of an O'Brien–Fleming trial; above the boundary a trial stops there. Highlighted are the outcomes that count as at least as extreme as the observed one when outcomes are ordered stagewise: at 80, z ≥ 4.56 (probability 2.54 × 10⁻⁶ with no effect); at 160, z ≥ 3.30 (probability 4.82 × 10⁻⁴ with no effect); at 240, none; at 320, none; at 400, none. The two-sided p-value is 9.69 × 10⁻⁴. Stopping rules

The outcomes a trial could have stopped with

A trial that stops at its second look with z = 3.3 has a two-sided p-value of 0.000969, 0.000987, 0.00187 or 0.0421, depending on how the outcomes it could have stopped with are ordered. One of the four orderings does not change when the looks the trial never reached are replanned, and the same one gives a trial that ran to the end with z = 6 a p-value of 0.0256.

 The false discovery rate of twenty correlated tests, against the correlation. BH, every null true: 5.08% at 0, 4.86% at 0.3, 3.70% at 0.6, 2.34% at 0.9. BH, 10 of 20 real: 2.55% at 0, 2.53% at 0.3, 2.26% at 0.6, 1.66% at 0.9. BY, every null true: 1.46% at 0, 1.31% at 0.3, 1.03% at 0.6, 0.69% at 0.9. BY, 10 of 20 real: 0.72% at 0, 0.75% at 0.3, 0.64% at 0.6, 0.50% at 0.9. 20,000 families at each correlation. Corrections, and what each controls

False discoveries that arrive together

Correlate twenty tests and Benjamini–Hochberg still holds its false discovery rate — 1.66% at a correlation of 0.9 with ten real effects, against 2.55% when the tests are independent. What changes is how the errors come. A family of true nulls reports anything 2.34% of the time instead of 5.08%, and when it does, it reports 16.56 false findings out of twenty.

 Thirty surveys of a thousand people, true prevalence 0.1%, test 90% sensitive and 95% specific. Each row is one survey. The thin bar is the corrected estimate ± 1.96 standard errors; the heavy bar is the exact interval for the positive rate carried through the correction; the tick is the one-sided 95% upper limit. 30 of the thirty printed intervals reach below zero and 0 lie entirely below it. Two tests, a threshold, and the rate they are read against

An upper limit is the finding

A survey of a thousand people with a 90%-sensitive, 95%-specific test, estimating a prevalence of one in a thousand, prints a corrected 95% interval that covers the truth 95.17% of the time — and reaches below zero in 97.95% of surveys and lies entirely below zero in 3.35%. Coverage is not the problem. The problem is what the interval can say, and the honest summary of a typical survey is one number: the prevalence is below 1.64%. Bringing that limit down to twice the truth takes 184,521 people at this specificity and 5,083 with a test that never gives a false positive.

 A premium that shrinks and a payout that grows. The relative error of the trapezoid's implied variance with the block length read off the whole autocovariance sequence, minus its error with the length from the lag-one correlation alone, in points, at four sample sizes and 400 draws each. Under the first-order autoregression the plug-in's algebra was written for, reading the sequence costs 3.87 at 120, 2.37 at 240, 2.48 at 480, 1.15 at 960. Under the five-period moving average, which the plug-in reads as an autoregression at 0.8, it is 0.43 at 120, -1.72 at 240, -3.01 at 480, -3.77 at 960 — a tie at 120 rows and a gain that grows with the sample. A block length chosen from the data

A length read off the whole sequence

The automatic block length reads one number off the sample — the lag-one correlation — and assumes the rest of the dependence is geometric. Reading the whole autocovariance sequence instead removes the assumption and puts the length where the law wants it. At a hundred and twenty rows it buys nothing, even where the assumption is wrong; it costs 3.87 points where the assumption is right, and by 960 rows that premium is 1.15 and the payout under a law the plug-in misreads is 3.77. Under long memory neither rule, nor the best length on each draw, gets within ninety per cent.

 An overlap that depends on where it is read. Each pair's overlap on the doubled log-likelihood scale — the sum of the two searches' own quantiles less the joint search's quantile, as a share of the smaller — at five quantile levels, over 1200 draws under the first-order autoregression. two dictionaries of independent columns: -0.176 at 0.5, -0.000 at 0.75, 0.238 at 0.9, 0.320 at 0.95, 0.538 at 0.99; a break, and an independent column: -1.211 at 0.5, -0.406 at 0.75, -0.029 at 0.9, 0.205 at 0.95, 0.161 at 0.99; two dictionaries of step columns: -0.397 at 0.5, 0.145 at 0.75, 0.438 at 0.9, 0.541 at 0.95, 0.682 at 0.99; a break, and a step column it contains: 1.000 at 0.5, 1.000 at 0.75, 1.000 at 0.9, 1.000 at 0.95, 1.000 at 0.99; a break, and a whitening window: 0.512 at 0.5, 0.640 at 0.75, 0.716 at 0.9, 0.732 at 0.95, 0.754 at 0.99. Every pair but the containment overlaps more at the 95% point than at the median, the control included. Two searches over different features

The overlap at the critical value

Two searches over independent noise columns are additive on average: the joint search finds what the two find separately, to within a fortieth of the smaller. At the point a test actually uses — the joint statistic's 95% quantile — the same pair overlaps by 0.320, and a rule that adds the two searches' own critical values rejects 1.4% of true nulls instead of 5%. Nothing about the searches causes it. Quantiles of a sum do not add, and on every pair in the field the overlap at the critical value is larger than the overlap the field measured.

 The same error, caught or invisible, by how it is arranged. The overidentification test's rejection rate against the error the violation actually puts into the estimate, so the two rows are the same estimate being equally wrong. With the whole violation on one instrument the test keeps its size at 5.0% under the null and reaches 86.4% by an error of 0.800. With both instruments violating in the same ratio as their first stages the two Wald ratios are identical, the test has nothing to compare, and it rejects at 5.8% at that same error — its own size. Over 1000 draws of 300 rows at each setting, at a nominal 5.0%. The test is a comparison between instruments and it was never a check on either. A variable that moves one thing only

Two instruments that disagree

The overidentification test keeps its size at 5.0% and reaches 86.4% power against a violation carried by one instrument. Against the same error carried by both in proportion to their first stages it rejects on 4.6% of draws — its own size — while the estimate is wrong by 0.3000, which is 94.2% of the confounding the instruments were brought in to remove.

 What a scale that grows across the sample costs. What the interval covers when the noise scale grows across the sample, against how far the departure has gone, over 1500 draws at each setting. The coverage runs from 94.47% at no departure to 83.93% at the end of the sweep, a loss of 11.07%. The rank argument needs the 200 calibration scores and the test score to be exchangeable, and this is one of the three ways that fails. A test built for it reaches 80% power at 4.054, where the coverage is 85.13% — so 9.87% of the loss is inside the region such a test would have missed. Coverage without a distribution

When the order matters

Three ways of breaking exchangeability cost 4.93, 11.07 and 1.07 points of coverage, and the ordering by cost is the reverse of the ordering by how soon a test would have caught them. The departure practitioners check for is the cheapest one.

 Twenty runs simulating an exactly 95% interval, checked every 250 replications. Each line is one run's running estimate; the dashed band is where the Wilson interval of the running estimate still contains 95%, and a run stops, marked, the first time it leaves the band. 8 of these twenty stop before 10,000 replications. The exact probability of stopping, from the recursion over the count, is 29.54%. What makes it checkable

A simulation that stops when it looks settled

A simulation of an interval that covers exactly 95%, checked every 250 replications for a significant departure and stopped when it finds one, flags that correct interval on 29.54% of runs. Stopped instead as soon as its estimate reaches 95%, it reports an interval that covers 94% as meeting its level on 37.21% of runs. Stopped when the estimate stops moving, it reports the right number — and has quietly chosen to run about fifteen hundred replications.

 Forty trials at a true effect of 0.16, under the rule "power at the trend < 10%". The upper line is the benefit boundary (4.56, 3.23, 2.63, 2.28, 2.04); the lower line is where the rule stops a trial for futility (0.40 at 80, 0.66 at 160, 0.95 at 240, 1.31 at 320). Of forty trials with a real effect, 29 cross for benefit and 11 are stopped for futility. Stopping rules

A boundary for giving up

Adding "stop if z is below zero" to an O'Brien–Fleming trial costs 5.20 points of power at the effect it was designed for and halves the observations a trial with no effect uses. Stopping when conditional power at the observed trend falls under 10% costs 13.23 points and stops 21.28% of trials with a real effect. Making that rule binding lowers the benefit boundary from 2.040 to 1.901, and a binding rule that is then ignored rejects a true null 3.523% of the time instead of 2.5%.

 Storey's estimate of the share of true nulls over twenty thousand families, independent and correlated at 0.6. The true share is 0.5. Independent tests: mean 0.610, spread 0.160, below half the truth in 0.92% of families. Correlated at 0.6: mean 0.609, spread 0.240, below half the truth in 9.33%. Corrections, and what each controls

Estimating how many nulls are true

Benjamini–Hochberg at 5% delivers 2.55% when half of twenty nulls are false, because it cannot tell how many are. Storey's estimate of that share, read off the p-values above one half, spends the rest and finds 81.93% of the real effects instead of 74.70% on independent tests. Correlated at 0.9, the same procedure reports a finding in 19.29% of families in which every null is true.

 An exact test rejecting a true hypothesis a fifth of the time. How often each analysis reports an effect when the average treatment effect is exactly zero and the effect varies between units, at 150 units with 25% treated. The permutation test on the difference in means reads 4.20% where the effect is constant — where the two nulls coincide and its exactness applies — and 22.93% where the effect varies with a standard deviation of 3. The same test on the studentised difference reads 6.27% there, and the ordinary large-sample t, which makes no exactness claim at all, reads 6.60%. The reference distribution the design supplies

The null the exactness is for

A permutation test is exact under the hypothesis that the treatment changed nothing for anybody. Under the hypothesis it changed nothing on average, with a quarter of the units treated and the effect varying between them, it rejects a true null 22.93% of the time.

 The bounded error and the unbounded one. How the sequential trace procedure's answer is distributed, against the sample length, for a three-series system with 2 genuine relations. Over-counting — claiming a stationary combination that is a random walk — reads 4.9%, 7.2%, 5.7%, 6.2%, 5.9%, 4.2% across the six lengths, never far from the 5% of a single test. Under-counting reads 69.5%, 40.2%, 14.0%, 0.5%, 0.0%, 0.0%. The procedure is described as a 5% rule and the 5% applies to one of those columns. Three series, and a count

The rank is a decision

The sequential procedure's 5% bounds one of its two errors. Over-counting reads between 4.2% and 7.2% at every sample length from fifty observations to three hundred; under-counting reads 69.5% at fifty and 0.0% at three hundred, and nothing in the procedure bounds it.

 Thirty winners of twenty analyses, effect 2 standard errors in one of them, with two intervals each. Each row is the largest of twenty statistics correlated at 0.6, reported because it cleared the family-wise threshold of 2.846. The thin grey bar is the ordinary interval, z ± 1.96; the heavy bar is the interval conditional on the statistic having won; the tick is the true mean of the analysis that won. Of these thirty the ordinary interval misses 1 and the conditional one 2; over 4,468 reported winners they cover 76.0% and 94.9%. The analyses that were available and not run

An interval for the winner

The largest of twenty correlated analyses, reported because it cleared the family-wise threshold, carries an ordinary 95% interval that covers its true mean 75.96% of the time at an effect of two standard errors and never at all when there is no effect. An interval built on the fact that it won covers 94.90% — and it is 2.48 times as wide, excludes zero for only 14.10% of the winners, and has no lower limit at all for 14.32% of them. It restores the promise by declining to say most of what the ordinary interval said.

 How often each one-number summary of a quantile plot detects five departures from normality, forty observations. Power at 5% against five departures. The straightness statistic detects the skewed source 83.3% of the time and light tails 30.8%; kurtosis detects light tails 72.5%; skewness detects light tails 0.2%. Rejecting when any of the five exceeds its own 5% point rejects 13.9% of normal samples; correcting that search to 5% gives it 37.4% power against light tails. What a diagnostic plot is showing

A number for the shape

Five one-number summaries of a quantile plot's departure from its line, each a correct 5% test on forty normal observations, and no two agree about what matters. Straightness catches a skewed source 83% of the time and light tails 31%; kurtosis catches light tails 72% and skewness 0.2%. Reading all five and reporting whichever looks bad rejects 13.9% of genuinely normal samples. Correcting that search to 5% costs almost half the power against the departure it would have caught best — and a line-up of twenty panels gets the same correction for nothing.

 Five length rules, read on two intervals. The coverage of a 95% interval for the mean at the block length each of five rules picks, on the percentile interval and the studentised one built from the same resamples, under a five-period moving average and a first-order autoregression at 120 and 480 rows, 2,000 draws each. moving average at 120: percentile 88.5%, 85.3%, 86.4%, 86.3%, 88.8%; studentised 92.5%, 87.9%, 94.9%, 92.6%, 95.0%. moving average at 480: percentile 90.5%, 90.5%, 90.4%, 91.6%, 91.9%; studentised 91.2%, 91.2%, 94.5%, 93.3%, 95.0%. autoregression at 120: percentile 84.7%, 80.2%, 85.8%, 85.4%, 85.9%; studentised 90.3%, 83.0%, 92.0%, 90.9%, 94.3%. autoregression at 480: percentile 87.0%, 87.0%, 90.0%, 90.5%, 90.9%; studentised 88.6%, 88.6%, 93.0%, 93.0%, 94.2% (in the order eight, from the protocol, n to the one third, the plug-in, the whole sequence, the best on the draw). The rules spread over at most 5.6% on the percentile interval; the interval moves the same rule by up to 8.5%. A block length chosen from the data

A ranking that belongs to the interval

Five ways of choosing a block length, read on the coverage of the interval each length builds rather than on the variance it implies. On the percentile interval the variance's verdict mostly survives: under a five-period moving average the plug-in and the whole-sequence rule tie at 120 rows and the sequence rule leads at 480. Build a studentised interval from the same resamples and the lead reverses — the plug-in's longer blocks cover 94.5% against 93.3% — because an interval that widens for its own uncertainty is paid for a long block where a percentile interval is punished for one. Under long memory no length and no interval covers two times in three.

 Four rules for a break and a column. How often each of four rules says something is there, for the pair of searches over a break and a column, under four truths, 400 draws each under a first-order autoregression at 0.8 with every threshold simulated on 1200 null draws. nothing there: joint, calibrated 6.3%, summed thresholds 6.3%, Bonferroni 6.3%, either, uncorrected 12.0%. a shift from row 60: joint, calibrated 65.3%, summed thresholds 64.0%, Bonferroni 55.0%, either, uncorrected 69.3%. a column enters: joint, calibrated 11.8%, summed thresholds 10.8%, Bonferroni 73.0%, either, uncorrected 82.5%. both: joint, calibrated 70.5%, summed thresholds 69.0%, Bonferroni 75.8%, either, uncorrected 84.8%. The first block is each rule's size. Two searches over different features

The power a calibration buys

Charging two searches separately makes a joint test conservative, and calibrating the joint statistic on its own null repairs that. Against a real break, on the pair of a break search and a window search, the repair is worth 47 points of power — 62.0% against 14.5%. On the pair of a break search and a column search it is worth nothing, and worse: a real column is found by the calibrated joint test on 11.8% of samples and by a plain Bonferroni split on 73.0%, because a joint statistic takes its threshold from the noisier of its two searches.

 What the main effects cost the rest of the model. The compound optimum of α·log Ds(main effects) + (1 − α)·log D on an 11-by-11 grid of the square, at 14 weights from α = 0 to 0.999, placed by its D-efficiency against the D-optimal design and its main-effect efficiency against the factorial. The D-optimal design (α = 0) gives the main effects 74.3% of what the factorial gives. At α = 0.5 the design keeps 94.6% of D and gives 85.6%; at 0.9, 64.9% and 96.8%; at 0.99, 31.3% and 99.7%. The factorial itself (open, at the left edge) gives the main effects everything and the model nothing: its D-efficiency is zero. The design optimal for the two quadratic terms (red) gives the main effects 50.0% at a D-efficiency of 83.6%. The criterion, and what it assumes

A subset the model cannot use

Nominate the two main effects of a quadratic response surface as the parameters of interest and the optimal design is the 2² factorial — four corners, a quarter each — which leaves the full model's information matrix at rank four of six and the curvature with nothing at all. That is why the field's algorithm would not converge on it. Approached through a compound criterion instead, the trade is explicit: 96.8% of the main effects' best information costs the model 35% of its D-efficiency, and the last three points cost nine tenths of what is left about the curvature.

 The rows are held fixed; only the clusters move. Counted coverage of four 95% intervals for a slope, at five cluster counts with the row count held at 300 throughout and a within-cluster correlation of 0.1, over 6000 draws apiece, with the sizes equal. The interval that counts rows covers 53.42% at 5 clusters — a second closed form says 2Φ(z/√D) − 1 = 54.44% for a design effect of 6.900, and reads nothing about clusters at all. The cluster-robust interval read against a normal covers 74.43% there and 94.20% at 100 clusters; read against a t on G − 1 it covers 85.08% and 94.47%. The number of independent things is the cluster count, and every quantity here is blind to how many rows were typed. A standard error for a model that is wrong

The count that is not the rows

Three hundred rows in five clusters of sixty carry 6.9000 times the variance an independent-rows calculation reports, and the interval that counts rows covers 53.42%. The same five unequal sizes laid out two ways give design effects of 9.3158 and 5.4652.

 The chance of crossing later from each interim z, under six schedules with O'Brien–Fleming-type spending boundaries. Exact. At an interim |z| of 1.0: end only 3.64%, +0.6 3.41%, +0.75 3.20%, +0.9 3.41%, every 0.125 3.00%, every 0.05 2.88%. At 2.5: end only 38.30%, +0.6 45.80%, +0.75 45.36%, +0.9 41.70%, every 0.125 50.08%, every 0.05 53.24%. The heavy line is the largest of the six at each z. Stopping rules

A look the trend asked for

Under an O'Brien–Fleming-type spending function, every schedule of looks fixed in advance spends exactly 5.0000%. A committee that adds a look at three quarters of the trial whenever the interim z is 1.5 or more spends 5.2323% — 5.315% counted over a hundred thousand trials — and the most a committee choosing among six schedules could spend is 5.4390%.

 Twenty hypotheses tested in a declared order, the ten real effects listed first. Effects of three standard errors, ten real, familywise 5%. fixed sequence: 85.3% at position 1, 45.0% at 5, 20.4% at 10; overall power 46.10%; fallback: 49.1% at position 1, 56.4% at 5, 57.9% at 10; overall power 55.87%; Holm: 52.5% at position 1, 52.2% at 5, 52.5% at 10; overall power 52.53%. Corrections, and what each controls

An order that spends the error rate

Test twenty hypotheses in a declared order, each at the full 5% and each only if every one before it was rejected, and the first is found 85.3% of the time where Holm finds it 52.5%. The tenth is found 20.4% of the time, the product of the powers before it. Move one true null to the head of the list and every real effect behind it is found no more than 4.3% of the time.

 What each wrong count costs, 4 steps ahead. Squared forecast error 4 steps ahead at each imposed rank, relative to the correctly specified fit, at 200 observations. With 1 genuine relations, imposing 0 costs 13.3% and imposing 2 costs 4.8%. With 2 genuine relations, imposing 1 costs 15.6% and imposing 3 costs 2.5%. Under-counting is the more expensive mistake in both systems, and it is the one the procedure's level does not bound. Three series, and a count

Which mistake about the rank costs

On a system with two relations, imposing none costs 29.2% of squared forecast error and imposing three costs 2.5%. The expensive mistake is under-counting, which is the error the procedure's 5% does not bound — so the guarantee protects the cheap side.

 How often a winner's interval covers when it is conditioned on the analyses admitted to rather than the twenty run, effect 2. The largest of twenty analyses reported when it clears the family-wise threshold for the k admitted. At k = 1, 2, 5, 10, 20: correlation 0.6, conditioned on the admitted family 95.3%, 94.9%, 94.9%, 95.1%, 95.2%; the ordinary interval 67.7%, 71.1%, 75.2%, 76.5%, 76.1%; correlation 0, conditioned on the admitted family 94.4%, 95.6%, 95.3%, 94.8%, 95.6%; the ordinary interval 47.2%, 52.0%, 58.9%, 62.4%, 64.7%. The analyses that were available and not run

An interval for the analyses admitted to

An analyst runs twenty correlated analyses, admits to one, and reports the winner with an interval conditioned on having cleared 1.96 — the selection as written down rather than as it happened. The interval covers 95.3% of the time, against 95.6% for one conditioned on all twenty, because a winner's truncation point is set by the threshold far more than by its unseen rivals: hiding nineteen analyses moves it by 0.061 on average. What the hiding changes is how often there is a winner at all. With no effect anywhere, a family of twenty produces a reported winner 35.5% of the time against 4.8% when all twenty are admitted, and one whose interval excludes zero 1.48% of the time against 0.13% — the interval keeps its promise and the report does not.

 What a stated power leaves of the main effects. The main-effect efficiency of the best design reaching a power of 0.8 at level 0.05, as N|δ|² rises through 50, 60, 70, 80, 100, 130, 170, 220, 300, 400, 600, 1000. a saddle, two-degree test, the compound path: out of reach, out of reach, 0.806, 0.841, 0.881, 0.914, 0.937, 0.952, 0.966, 0.975, 0.984, 0.990. a bowl, two-degree test, the compound path: 0.782, 0.823, 0.851, 0.871, 0.898, 0.923, 0.941, 0.955, 0.967, 0.976, 0.984, 0.990. a bowl, its own one-degree contrast, the compound path: 0.827, 0.859, 0.880, 0.896, 0.918, 0.937, 0.952, 0.964, 0.973, 0.980, 0.987, 0.992. a bowl, a factorial with centre points: 0.914, 0.930, 0.940, 0.948, 0.959, 0.969, 0.976, 0.982, 0.987, 0.990, 0.993, 0.996. The criterion, and what it assumes

A design chosen for a test

A screening experiment that wants its main effects and a check on curvature has been told to trade them with a weight nobody can state. It can state a power. Read against a power of 80%, the frontier collapses to one curve in runs times the squared size of the curvature, and a large enough trial pays almost nothing. But the curve belongs to a test. A factorial with a few centre points beats the entire frontier on a bowl, spending 4% of its main-effect information where the frontier spends 10%, and against a saddle of the same size it has exactly its 5% level.

 What Hansen's threshold costs, by the number of origins. The power a threshold of zero keeps over Hansen's, on the same draws. with sixteen stale candidates: 7.5%, 7.3%, 6.8%, 5.3%, 3.7%, 6.0%. the eight windows alone: 2.7%, 3.8%, 4.7%, 5.3%, 3.7%, 6.0%, at 25, 50, 100, 200, 400, 800 origins. The price does not fall as the sample grows. The best of a set, and what the search costs

A threshold against its sample

A recentred test's threshold exists so that the test is right in the limit, and at a hundred origins it costs a fifth of the power against a stale set. The natural defence is that the price falls as the sample grows and the guarantee starts to earn it. Across twenty-five to eight hundred origins it does not: a threshold of zero keeps four to seven points more power at every size, and its size at the corner stays at the level from fifty origins on. What does change with the sample is the stale set, which stops costing anything by two hundred origins under Hansen's rule and by eight hundred under the most cautious.

 Every finding Benjamini–Hochberg made in thirty families, with its interval, at real effects of 2. 85 findings, sorted by their estimate. 12 of their ordinary 95% intervals miss the true effect, every one of them on the far side; 6 of the wider false-coverage-rate intervals miss. Corrections, and what each controls

Intervals for the findings

Benjamini–Hochberg's findings usually go out each with its ordinary 95% interval. With ten real effects of two standard errors among twenty tests, 11.59% of those intervals miss their effect, every miss on the far side, and the interval around the most prominent finding covers 72.36% of the time — 2.38% when the effects are one standard error. Intervals widened for the number of findings hold the share that miss under 5%.

 Three detectors for one departure, all at 5%. How often each of three checks on the calibration scores fires, against the size of the drift, with every critical value simulated under no drift so that all three sit at 5.0% exactly. The incumbent — a rank comparison of the first half of the scores against the second — reaches four-in-five power at a growth factor of 4.31. Reading each score's rank against its position reaches it at 2.65, and the largest running departure of the scores from their mean at 2.12. The ordering of the three is the ordering by how much of the sample's arrangement each one uses. Coverage without a distribution

A detector built for the ordering

The best of three checks for a drifting scale fires at half the growth factor the standard one needs — 2.12 against 4.31 — and still leaves 6.50 points of coverage gone before it does, against 0.51 for serial correlation. The reversal was not a property of the test.

 One estimator, three answers, and only the reference changes. Coverage of the cluster-robust 95% interval for the slope against the number of clusters, at 30 rows in each. The estimator is identical in all three curves; what differs is the number it is compared against. At 5 clusters it covers 75.05% against a normal, 85.30% against a t on 4 degrees of freedom and 87.95% against a t on 3. At 80 clusters the three agree to within a point. The correction costs nothing: the same standard error, a different table. A standard error for a model that is wrong

The reference the sandwich is read against

The cluster-robust interval covers 75.05% at five clusters and 93.58% at eighty. The same estimate read against a t on G − 2 covers 87.95% at five, and the estimator is unchanged — three hundred rows grouped into five clusters cover 74.28% where the same three hundred grouped into seventy-five cover 94.63%.

 How often a diagnostically chosen winner's interval excludes zero, against the diagnostic's correlation with the statistic, no effect anywhere. Twenty analyses correlated 0.6; the one with the best diagnostic is reported if it clears 1.96. A winner is reported in 2.52%, 4.16%, 6.81%, 13.07% of families at γ = 0, 0.3, 0.6, 0.9. Its interval excludes zero: conditioned on clearing 1.96, 1.69%, 2.28%, 3.30%, 4.69%; conditioned on the diagnostic as well, 1.69%, 1.86%, 1.83%, 2.31%; rebuilt from the report's three numbers, 1.69%, 1.80%, 2.13%, 2.52%. The analyses that were available and not run

A winner chosen by its diagnostic

An analyst runs twenty correlated analyses and reports the one whose diagnostics look cleanest, if it clears 1.96 — and a diagnostic is rarely independent of the statistic it checks. With no effect anywhere, a diagnostic correlated 0.9 with its statistic lets a winner through in 13.07% of families against 2.52% for an unrelated one. The interval conditioned on clearing 1.96 still covers 93.27%, but its misses have moved: it excludes zero in 4.69% of reported winners, where a 95% interval should do so in 2.5%. Conditioning on the diagnostic as well repairs it, and so does something a reader can do alone — weight the reported statistic by the chance an analysis like it wins the diagnostic contest, from three numbers a report can state.

 Power against expected observations for two fixed schedules and a look added when the interim trend is close, O'Brien–Fleming-type spending. Straight to the end: power 89.01% at 338.9 expected observations. A look at three quarters always: 88.38% at 300.7. Adding the look when the interim |z| is at least 0.5, 1, 1.5, 2, 2.5, with boundaries corrected to spend exactly 5%: 88.41% at 302.0; 88.45% at 302.8; 88.56% at 306.3; 88.76% at 315.5; 88.95% at 330.1. With the spending boundaries alone the same rules spend 5.059%, 5.156%, 5.232%, 5.187%, 5.058%. Stopping rules

A look added by a rule

A monitoring committee's rule — add a look at three quarters of the trial when the interim |z| is at least 1.5 — spends 5.2323% under O'Brien–Fleming-type boundaries. Written into the protocol it can be given boundaries that spend exactly 5%, three ways: raise the added look's boundaries by 2.05%, raise every later boundary by 0.99%, or preserve at each interim value the chance of crossing the plan would have given. All three cost the same, 0.38 points of power, from 88.94% to 88.56%, because the power the uncorrected rule seemed to keep was the excess error. Made exact, the rule is a design between the two fixed schedules — and at every trigger slightly better than randomising between them.

 How often an adjusted permutation test rejects a true sharp null when the covariate is chosen after randomisation, held fixed or re-chosen inside every shuffle. 150 units, half treated, six baseline covariates of which three are prognostic; 2,000 experiments and 99 re-randomisations each, read on the adjusted coefficient over its sandwich standard error. Choosing the covariate most correlated with the outcome: 5.0% held fixed, 5.0% re-chosen. Choosing the covariate with the smallest residual variance: 5.5% held fixed, 5.3% re-chosen. Choosing the covariate most imbalanced between the arms: 3.0% held fixed, 4.8% re-chosen. Choosing the covariate with the smallest adjusted p-value: 10.4% held fixed, 5.1% re-chosen. Naming the most prognostic covariate in advance: 5.5%. The reference distribution the design supplies

The covariate chosen afterwards

A trial that chooses which baseline covariate to adjust for after randomisation has two ways to run its permutation test: hold the chosen covariate fixed across the shuffles, which is what an analysis that reports adjusting for it actually does, or re-make the choice inside every shuffle, which is exact by construction. Holding it fixed, a choice made on significance rejects 10.4% of true sharp nulls, a choice made on imbalance only 3.0% — and pays seven and a half points of power for it — while a choice that never reads the arm labels is the same test either way. The interacted, studentised statistic keeps its weak-null behaviour only when the covariate chosen is the one the effect varies with.

 How often two orders of selection report a winner from twenty analyses, no effect anywhere. Twenty analyses correlated 0.6, each with a diagnostic correlated γ with its own statistic, over 20,000 families a point. Clean then significant — the best diagnostic, reported if it clears 1.96 — reports a winner in 2.55%, 4.25%, 6.91%, 13.15% of families at γ = 0, 0.3, 0.6, 0.9. Significant then clean — the best diagnostic among those that cleared 1.96 — reports one in 18.04%, 18.04%, 18.04%, 18.05%: in every family where any analysis cleared 1.96, whatever γ is. The analyses that were available and not run

The cleanest of the significant

An analyst with twenty correlated analyses can choose in two orders: take the one whose diagnostics look cleanest and report it if it clears 1.96, or take the analyses that cleared 1.96 and report the cleanest of them. The second is the one described as prudent, because it never reports an analysis whose diagnostics look poor — and with no effect anywhere it reports a significant analysis in about 18% of families whatever the diagnostic is, against 2.55% to 13.15% for the first. The diagnostic decides which significant analysis is shown and never whether one is. Each report's own interval can still be made exact, by conditioning on a selection set that is a union of pieces rather than a half-line; the family's claim count cannot.

 How much a look added at three quarters spends above 5%, by what the decision to add it reads. A two-look O'Brien–Fleming-type trial, interim at half the information, adds a look at three quarters with a stated chance under the null. Its error rate above 5%, in points, at chances of 0.1, 0.3, 0.5, 0.7, 0.9: triggered by a calendar or by the within-arm variance, 0.00000, 0.00000, 0.00000, 0.00000, 0.00000; by the blinded variance with 20 patients at the interim, 0.034, 0.064, 0.069, 0.054, 0.022; with 100, 0.014, 0.027, 0.030, 0.025, 0.012; with 1,000, 0.004, 0.008, 0.009, 0.008, 0.004; by the interim trend itself, 0.234, 0.163, 0.089, 0.042, 0.013. Exact integrals over the interim statistic. Stopping rules

The variance that contains the effect

A monitoring committee that adds an interim look because the variance came in higher than planned is reading a nuisance quantity, and a look added on a nuisance quantity should leave the spending function's 5% alone. It does, exactly, when the variance is the within-arm one, and when the trigger is the calendar. It does not when the variance is the one computed with the arm labels hidden: that variance is the within-arm sum of squares plus the interim statistic squared, so it is high more often when the trend is strong. The excess is 0.069 points at a twenty-patient interim and falls as one over the square root of the interim's size — a tenth of what a trigger on the trend itself spends, and never zero.

 Six intervals, one of them honest by right. How often each rule's reported 95% interval covers the true coefficient under a five-period moving average, over 1000 draws of 120 rows, for the most persistent predictor (its own lag-one correlation 0.9) and the white one. The law's own covariance covers 94.9% and 94.1%. Least squares covers 65.0% on the persistent predictor and 94.4% on the white one; a fitted ρ̂ 93.0% and 96.3%; the two-step band 86.5% and 97.3%. The joint fit covers 73.2% and 71.4% — short on both, and short on the white predictor where every other rule is honest — and counting its eight band coefficients gives 76.1% and 73.5%. When the observations repeat each other

The interval the joint fit reports

Maximising a regression's coefficients and an eight-lag covariance band together gives a better estimate than plugging the band in. It also prints an interval that covers 71% to 85% of the time where 95% was promised — on the white predictor too, where every other rule is honest — because it reports almost the precision of the law's own covariance and delivers less than half of that advantage. Counting the eight lags it estimated buys one to two points.

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