Evidence map›Paper›PMID 40777734›Full record

ArticleThe American statistician2025

Counternull sets in randomized experiments.

M-A C Bind, D B Rubin

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In one paragraph

Article in The American statistician, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

M-A C BindBiostatistics Center, Massachusetts General Hospital, Boston, MA, USA.
D B RubinYau Center for Mathematical Sciences, Tsinghua University, Beijing, China.

Funding

Transporting established insights from classical experimental design to address causal questions in environmental epidemiology including the understanding of biological mediating mechanismsDP5OD021412 · OD · MASSACHUSETTS GENERAL HOSPITAL · PI BIND, MARIE-ABELE CATHERINE · 2016 to 2020
$2.1M
Statistical Designs and Methods for Double-Sampling for HIV/AIDSR01AI102710 · NIAID · JOHNS HOPKINS UNIVERSITY · PI FRANGAKIS, CONSTANTINE E · 2013 to 2016
$1.6M
NIAID NIH HHS R01 AI102710NIH HHS DP5 OD021412
6 · The paper itself

Abstract

Consider a study whose primary results are "not statistically significant". How often does it lead to the following published conclusion that "there is no effect of the treatment/exposure on the outcome"? We believe too often and that the requirement to report counternull values could help to avoid this! In statistical parlance, the null value of an estimand is a value that is distinguished in some way from other possible values, for example a value that indicates no difference between the general health status of those treated with a new drug versus a traditional drug. A counternull value is a nonnull value of that estimand that is supported by the same amount of evidence that supports the null value. Of course, such a definition depends critically on how "evidence" is defined. Here, we consider the context of a randomized experiment where evidence is summarized by the randomization-based p-value associated with a specified sharp null hypothesis. Consequently, a counternull value has the same p-value from the randomization test as does the null value; the counternull value is rarely unique, but rather comprises a

Indexed as

Fisher-exact p-valueHypothesis testingRandomization-based inferenceRandomization inference

Identifiers

PMID40777734
PMCPMC12326273

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.