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Holm Step-Down Familywise Error Control

1 · Prerequisites

2 · Summary

This page defines Holm's step-down procedure for a finite hypothesis family fixed before observing the data, verifies that its rejection events are measurable, and proves strong familywise error control. The proof identifies the earliest-ranked true null: any false rejection forces that null's p-value below α/m0, where m0 is the number of true nulls. Bonferroni control on the true-null subfamily then gives an error probability at most α, with no independence assumption. The guarantee concerns false rejections; the procedure itself does not provide simultaneous confidence intervals. Choosing the family after seeing the p-values is outside the theorem unless validity remains established for that selection.

3 · Logical flowchart

4 · Definitions, theorems and proofs

AlgorithmAlgorithm: Literature-sourcedProof: Not applicablejudge pass (gpt-6-luna)Open item page →

Holm step-down procedure

Definition

For m≥1, fix a finite family of null hypotheses H0i, 1≤i≤m, before observing the data, and let pi:X→[0,1] be p-value statistics valid for their respective nulls in the sense of Null-valid p-values and familywise and false-discovery error rates. Given α∈[0,1] and an observation x∈X, order the indices by the pairs (pi(x),i) in lexicographic order. This gives a unique permutation π with pπ(1)(x)≤⋯≤pπ(m)(x); ties are ordered by increasing index.

Set rα(x)=max⁡{k∈{0,…,m}:pπ(j)(x)≤α/(m−j+1) for every 1≤j≤k}. The rejected-index set is Rα(x)={π(j):1≤j≤rα(x)}. Reject H0i exactly when i∈Rα(x); equivalently, compare sequentially to α/(m−j+1), stop at the first failed comparison, and reject only the preceding ranks.

For m=0, extend the procedure to the empty input by returning the empty rejected set. For m≥1, every denominator in a tested rank is positive. The ordering is a unique finite permutation, and the finite set defining rα(x) contains 0, so the output is well-defined.

LemmaStatement: AI-adaptedProof: AI-adaptedprecheck passOpen item page →

Holm rejection events are measurable

Statement

Let m≥1, let p1,…,pm:X→[0,1] be p-value statistics on a measurable data space (X,A), and fix α∈[0,1]. Define Rα(x) by the lexicographic ordering and passing-prefix formulas of Holm step-down procedure, applied here to these statistics whether or not they are null-valid. These formulas are well-defined for every input in [0,1]m: the ordering is unique, the tested denominators are positive, and the admissible prefix lengths include 0. Then each rejection event Di(α):={x∈X:i∈Rα(x)} belongs to A.

Facts & Assumptions

Given: A measurable data space, finitely many p-value statistics pi, and α∈[0,1].

[F1]

Valid p-values: a p-value statistic is a statistic p:X→[0,1], where [0,1] carries the trace of the Borel sigma-algebra.

[F2]

Statistics and estimators: a statistic is a measurable map from the data space to its target measurable space.

[F3]

The ordering and prefix formulas of Holm step-down procedure order the pairs (pi(x),i) lexicographically and return the prefix whose rank cutoffs all pass. Here these formulas define Rα on all the given measurable inputs; on null-valid inputs they agree with the cited procedure. Null validity is not needed for measurability.

[F4]

Arithmetic and lattice operations preserve measurability whenever they are defined: sums and scalar multiples of measurable real-valued functions are measurable whenever defined; hence differences of measurable real-valued functions are measurable.

[F5]

A measurable function between measurable spaces: a map is measurable when the preimage of each measurable target set belongs to the source sigma-algebra.

[F6]

Sigma-algebras: a sigma-algebra is closed under finite intersections and finite unions.

Proof

technique · direct
1.1F1F2F3F4F5F6construct

By [F1] and [F2], each pi is measurable into [0,1] with its Borel trace. For indices a,b, the difference pa−pb is measurable by [F4], so {pa≤pb} and {pa<pb} are measurable preimages by [F5] of the Borel half-lines (−∞,0] and (−∞,0). For each permutation π, its ordering cell Cπ is the finite intersection over positions a<b of {pπ(a)≤pπ(b)} if π(a)<π(b) and {pπ(a)<pπ(b)} otherwise. These cells are measurable by [F6], and every x belongs to exactly one because lexicographic ordering with index tie-breaking gives a unique permutation by [F3]. For rank j, let Qπ,j={pπ(j)≤α/(m−j+1)}; this pass event and its failure event Qπ,jc are measurable preimages of Borel half-lines by [F1], [F2], and [F5]. For k∈{0,…,m}, intersect Cπ with the pass events for j≤k and, when k<m, the failure event at k+1. Empty intersections are omitted when k=0 or k=m. The resulting measurable cell is exactly the event that the order is π and the returned prefix length is k, by [F3]; these finitely many cells partition X.

2.1F3F6step 1.1construct∎

The event Di(α) is the union of the cells from step 1.1 over all permutations and prefix lengths k with i∈{π(1),…,π(k)}. This is a finite union because there are m! permutations and m+1 prefix lengths. By [F6], Di(α)∈A for every i.

TheoremStatement: Literature-sourcedProof: AI-adaptedprecheck passOpen item page →

Holm step-down controls strong FWER

Statement

Let m≥1 and let a fixed finite family of null hypotheses and valid p-value statistics be as in Null-valid p-values and familywise and false-discovery error rates. For α∈[0,1], let Rα(X) be the rejected-index set from Holm step-down procedure, and let I0(θ) be the true-null index set defined there. By Holm rejection events are measurable, the rejection events are measurable, so the familywise error rate is well-defined as FWER⁡θ=Pθ(Rα(X)∩I0(θ)≠∅). Then FWER⁡θ≤α for every θ∈Θ. This is strong familywise error control and requires no independence or other restriction on the joint distribution of the p-values.

Facts & Assumptions

Given: A statistical experiment, a fixed finite family with valid p-values in the sense of Null-valid p-values and familywise and false-discovery error rates, and α∈[0,1]. The Holm rejected set is the initial ordered prefix passing its step-down cutoffs.

[F1]

Null-valid p-values and familywise and false-discovery error rates: for each i, validity means Pθ(pi(X)≤u)≤u for every θ∈H0i and u∈[0,1].

[F2]

Null-valid p-values and familywise and false-discovery error rates: I0(θ)={i:θ∈H0i} and FWER is the probability of at least one rejected true null.

[F3]

Holm step-down procedure: the procedure orders p-values, compares rank j with α/(m−j+1), and rejects exactly the prefix whose comparisons all pass.

[F4]

Holm rejection events are measurable: each Holm rejection event is measurable, so the FWER definition in [F2] applies.

[F5]

Strong familywise error control by Bonferroni: the equal-weight one-step Bonferroni rule on any fixed family of q≥1 valid p-values has strong FWER at most α when it rejects at cutoff α/q, under arbitrary dependence.

Proof

technique · direct
1.1givenF2F3F4choosealgebra

Fix θ and put I0=I0(θ) and m0=∣I0∣. By [F4], the Holm rejection events are measurable, so the FWER from [F2] is defined. If m0=0, no true null can be rejected. Otherwise, let j0 be the least rank occupied by a true null in the ordering from [F3]; it exists because I0 is finite and nonempty. Since [F3] rejects only a prefix, any rejection of a true null forces rank j0 to be rejected. Thus pπ(j0)(X)≤α/(m−j0+1). All m0 true nulls occur at ranks j0,…,m, so m−j0+1≥m0 and α/(m−j0+1)≤α/m0. Hence any false rejection implies ⋃i∈I0{pi(X)≤α/m0}.

2.1F1F2F5step 1.1algebra∎

If m0≥1, fix θ and consider the fixed subfamily indexed by I0(θ); it has m0 members, all true under this θ, and it is not selected from the observed p-values. By [F1] its p-values are valid. Applying the Bonferroni theorem [F5] to this subfamily at level α bounds the probability of its threshold-union event ⋃i∈I0{pi(X)≤α/m0} by α. This contains the Holm false-rejection event from step 1.1, so FWER⁡θ≤α. If m0=0, step 1.1 gives FWER zero. Since θ was arbitrary, the control is strong.

5 · Examples, counterexamples and false statements

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