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Article ; Online: Reproducibility, p-values, and type III errors: response to Mayo (2022).

Stark, Philip B

Conservation biology : the journal of the Society for Conservation Biology

2022 Volume 36, Issue 5, Page(s) e13986

MeSH term(s)	Conservation of Natural Resources ; Reproducibility of Results
Language	English
Publishing date	2022-08-18
Publishing country	United States
Document type	Journal Article
ZDB-ID	58735-7
ISSN	1523-1739 ; 0888-8892
ISSN (online)	1523-1739
ISSN	0888-8892
DOI	10.1111/cobi.13986
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Book ; Online: Overstatement-Net-Equivalent Risk-Limiting Audit

Stark, Philip B.

ONEAudit

2023

Abstract: Card-level comparison risk-limiting audits (CLCAs) heretofore required a CVR for each cast card and a "link" identifying which CVR is for which card -- which many voting systems cannot provide. Every set of CVRs that produces the same aggregate results ... ...

Abstract	Card-level comparison risk-limiting audits (CLCAs) heretofore required a CVR for each cast card and a "link" identifying which CVR is for which card -- which many voting systems cannot provide. Every set of CVRs that produces the same aggregate results overstates contest margins by the same amount: they are overstatement-net-equivalent (ONE). CLCAs can therefore use CVRs from the voting system for any number of cards and ONE CVRs for the rest. Ballot-polling RLAs are equivalent to CLCAs using ONE CVRs. CLCAs can be based on batch-level results (e.g., precinct subtotals) by constructing ONE CVRs for each batch. In contrast to batch-level comparison audits (BLCAs), this avoids tabulating batches manually and works even when reporting batches do not correspond to physically identifiable batches of cards. If the voting system can export linked CVRs for only some ballot cards, auditors can still use CLCA by constructing ONE CVRs for the rest of the cards from contest results or batch subtotals. This obviates the need for "hybrid" audits. This works for every social choice function for which there is a known RLA method, including IRV. Sample sizes for BPA and ONEAudit using contest totals are comparable. ONEAudit using batch subtotals has smaller sample sizes than ballot-polling when batches are much more homogeneous than the election overall. Sample sizes can be much smaller than for BLCA: A CLCA of the 2022 presidential election in California at risk limit 5% using ONE CVRs for precinct-level results would sample ~70 ballots statewide, if the reported results are accurate, compared to about 26,700 for BLCA. The 2022 Georgia audit tabulated >231,000 cards versus ~1300 for ONEAudit. For data from a pilot hybrid RLA in Kalamazoo, MI, in 2018, ONEAudit gives a risk of ~2%, substantially lower than the 3.7% measured risk for SUITE, the method the pilot used. Comment: to appear in Proceedings of the 2023 Financial Cryptography Voting workshop
Keywords	Statistics - Applications ; Computer Science - Cryptography and Security
Subject code	519
Publishing date	2023-03-06
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: ALPHA

Stark, Philip B.

Audit that Learns from Previously Hand-Audited Ballots

2022

Abstract: BRAVO, the most widely tried method for risk-limiting election audits, cannot accommodate sampling without replacement or stratified sampling, which can improve efficiency and may be required by law. It applies only to ballot-polling audits, which are ... ...

Abstract	BRAVO, the most widely tried method for risk-limiting election audits, cannot accommodate sampling without replacement or stratified sampling, which can improve efficiency and may be required by law. It applies only to ballot-polling audits, which are less efficient than comparison audits. It applies to plurality, majority, super-majority, proportional representation, and ranked-choice voting contests, but not to many social choice functions for which there are RLA methods, such as approval voting, STAR-voting, Borda count, and general scoring rules. And while BRAVO has the smallest expected sample size among sequentially valid ballot-polling-with-replacement methods when reported vote shares are exactly right, it can require arbitrarily large samples when the reported reported winner(s) really won but reported vote shares are wrong. ALPHA is a simple generalization of BRAVO that (i) works for sampling with and without replacement and Bernoulli sampling; (ii) increases power for stratified audits by avoiding the need to use a $P$-value combining function or to maximize $P$-values over nuisance parameters within strata, and allowing adaptive sampling across strata; (iii) works not only for ballot-polling but also for ballot-level comparison, batch-polling, and batch-level comparison audits, sampling with or without replacement, uniformly or with weights proportional to size; (iv) works for all social choice functions covered by SHANGRLA; and (v) in situations where both ALPHA and BRAVO apply, requires smaller samples than BRAVO when the reported vote shares are wrong but the outcome is correct--five orders of magnitude in some examples. ALPHA includes the family of betting martingale tests in RiLACS, with a different betting strategy parametrized as an estimator of the population mean and explicit flexibility to accommodate sampling weights and population bounds that vary by draw.
Keywords	Statistics - Methodology ; Computer Science - Cryptography and Security ; Computer Science - Computer Science and Game Theory
Subject code	519
Publishing date	2022-01-07
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Non(c)esuch Ballot-Level Risk-Limiting Audits for Precinct-Count Voting Systems

Stark, Philip B.

2022

Abstract: Risk-limiting audits (RLAs) guarantee a high probability of correcting incorrect reported outcomes before the outcomes are certified. The most efficient use ballot-level comparison, comparing the voting system's interpretation of individual ballot cards ... ...

Abstract	Risk-limiting audits (RLAs) guarantee a high probability of correcting incorrect reported outcomes before the outcomes are certified. The most efficient use ballot-level comparison, comparing the voting system's interpretation of individual ballot cards sampled at random (cast-vote records, CVRs) from a trustworthy paper trail to a human interpretation of the same cards. Such comparisons require the voting system to create and export CVRs in a way that can be linked to the individual ballots the CVRs purport to represent. Such links can be created by keeping the ballots in the order in which they are scanned or by printing a unique serial number on each ballot. But for precinct-count systems (PCOS), these strategies may compromise vote anonymity: the order in which ballots are cast may identify the voters who cast them. Printing a unique pseudo-random number ("cryptographic nonce") on each ballot card after the voter last touches it could reduce such privacy risks. But what if the system does not in fact print a unique number on each ballot or does not accurately report the numbers it printed? This paper gives two ways to conduct an RLA so that even if the system does not print a genuine nonce on each ballot or misreports the nonces it used, the audit's risk limit is not compromised (however, the anonymity of votes might be compromised). One method allows untrusted technology to be used to imprint and to retrieve ballot cards. The method is adaptive: if the technology behaves properly, this protection does not increase the audit workload. But if the imprinting or retrieval system misbehaves, the sample size the RLA requires to confirm the reported results when the results are correct is generally larger than if the imprinting and retrieval were accurate.
Keywords	Computer Science - Cryptography and Security ; Statistics - Applications
Subject code	519
Publishing date	2022-07-04
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Before reproducibility must come preproducibility.

Stark, Philip B

Nature

2018 Volume 557, Issue 7707, Page(s) 613

MeSH term(s)	Animals ; Humans ; Mice ; Peer Review, Research/standards ; Reproducibility of Results ; Research Report/standards ; Software ; Terminology as Topic
Language	English
Publishing date	2018-05-21
Publishing country	England
Document type	Journal Article
ZDB-ID	120714-3
ISSN	1476-4687 ; 0028-0836
ISSN (online)	1476-4687
ISSN	0028-0836
DOI	10.1038/d41586-018-05256-0
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Book ; Online: Sets of Half-Average Nulls Generate Risk-Limiting Audits

Stark, Philip B.

SHANGRLA

2019

Abstract: Risk-limiting audits (RLAs) for many social choice functions can be reduced to testing sets of null hypotheses of the form "the average of this list is not greater than 1/2" for a collection of finite lists of nonnegative numbers. Such social choice ... ...

Abstract	Risk-limiting audits (RLAs) for many social choice functions can be reduced to testing sets of null hypotheses of the form "the average of this list is not greater than 1/2" for a collection of finite lists of nonnegative numbers. Such social choice functions include majority, super-majority, plurality, multi-winner plurality, Instant Runoff Voting (IRV), Borda count, approval voting, and STAR-Voting, among others. The audit stops without a full hand count iff all the null hypotheses are rejected. The nulls can be tested in many ways. Ballot-polling is particularly simple; two new ballot-polling risk-measuring functions for sampling without replacement are given. Ballot-level comparison audits transform each null into an equivalent assertion that the mean of re-scaled tabulation errors is not greater than 1/2. In turn, that null can then be tested using the same statistical methods used for ballot polling---but applied to different finite lists of nonnegative numbers. SHANGRLA comparison audits are more efficient than previous comparison audits for two reasons: (i) for most social choice functions, the conditions tested are both necessary and sufficient for the reported outcome to be correct, while previous methods tested conditions that were sufficient but not necessary, and (ii) the tests avoid a conservative approximation. The SHANGRLA abstraction simplifies stratified audits, including audits that combine ballot polling with ballot-level comparisons, producing sharper audits than the "SUITE" approach. SHANGRLA works with the "phantoms to evil zombies" strategy to treat missing ballot cards and missing or redacted cast vote records. That also facilitates sampling from "ballot-style manifests," which can dramatically improve efficiency when the audited contests do not appear on every ballot card. Open-source software implementing SHANGRLA ballot-level comparison audits is available.
Keywords	Statistics - Applications ; Computer Science - Cryptography and Security
Publishing date	2019-11-22
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Valid inferences about soil carbon in heterogeneous landscapes

Stanley, Paige / Spertus, Jacob / Chiartas, Jessica / Stark, Philip B. / Bowles, Timothy

Geoderma. 2023 Feb., v. 430 p.116323-

2023

Abstract: Using soil organic carbon (SOC) to generate carbon offsets requires reliably quantifying SOC sequestration. However, accuracy of SOC measurement is limited by inherent spatial heterogeneity, variability of laboratory assays, unmet statistical assumptions, ...

Abstract	Using soil organic carbon (SOC) to generate carbon offsets requires reliably quantifying SOC sequestration. However, accuracy of SOC measurement is limited by inherent spatial heterogeneity, variability of laboratory assays, unmet statistical assumptions, and the relatively small magnitude of SOC changes over time, among other things. Most SOC measurement protocols currently used to generate offsets for C markets do not adequately address these issues, threatening to undermine climate change mitigation efforts. Using analyses and simulations from 1,117 soil samples collected from California crop and rangelands, we quantified measurement errors and sources of uncertainty to optimize SOC measurement. We demonstrate that (1) spatial heterogeneity is a primary driver of uncertainty; (2) dry combustion assays contribute little to uncertainty, although inorganic C can increase error; (3) common statistical methods-Student's t-test and its relatives-can be unreliable for SOC (e.g. at low to medium sample sizes or when the distribution of SOC is skewed), which can lead to incorrect interpretations of SOC sequestration; and (4) common sample sizes (10-30 cores) are insufficiently powered to detect the modest SOC changes expected from management in heterogeneous agricultural landscapes. To reduce error and improve the reliability of future SOC offsets, protocols should: (1) require power analyses that include spatial heterogeneity to determine minimum sample sizes, rather than allowing arbitrarily small sample sizes; (2) minimize the use of compositing; (3) require dry combustion analysis, by the same lab for all assays; and (4) use nonparametric statistical tests and confidence intervals to control Type I error rates. While these changes might increase costs, they will make SOC estimates more accurate and more reliable, adding credibility to soil management as a climate change mitigation strategy.
Keywords	carbon sequestration ; climate change ; combustion ; rangelands ; soil ; soil management ; soil organic carbon ; spatial variation ; t-test ; uncertainty ; California ; Soil carbon ; Spatial heterogeneity ; Valid inference ; Measurement ; Carbon markets
Language	English
Dates of publication	2023-02
Publishing place	Elsevier B.V.
Document type	Article ; Online
Note	Use and reproduction
ZDB-ID	281080-3
ISSN	1872-6259 ; 0016-7061
ISSN (online)	1872-6259
ISSN	0016-7061
DOI	10.1016/j.geoderma.2022.116323
Database	NAL-Catalogue (AGRICOLA)

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Book ; Online: Adaptively Weighted Audits of Instant-Runoff Voting Elections

Ek, Alexander / Stark, Philip B. / Stuckey, Peter J. / Vukcevic, Damjan

AWAIRE

2023

Abstract: An election audit is risk-limiting if the audit limits (to a pre-specified threshold) the chance that an erroneous electoral outcome will be certified. Extant methods for auditing instant-runoff voting (IRV) elections are either not risk-limiting or ... ...

Abstract	An election audit is risk-limiting if the audit limits (to a pre-specified threshold) the chance that an erroneous electoral outcome will be certified. Extant methods for auditing instant-runoff voting (IRV) elections are either not risk-limiting or require cast vote records (CVRs), the voting system's electronic record of the votes on each ballot. CVRs are not always available, for instance, in jurisdictions that tabulate IRV contests manually. We develop an RLA method (AWAIRE) that uses adaptively weighted averages of test supermartingales to efficiently audit IRV elections when CVRs are not available. The adaptive weighting 'learns' an efficient set of hypotheses to test to confirm the election outcome. When accurate CVRs are available, AWAIRE can use them to increase the efficiency to match the performance of existing methods that require CVRs. We provide an open-source prototype implementation that can handle elections with up to six candidates. Simulations using data from real elections show that AWAIRE is likely to be efficient in practice. We discuss how to extend the computational approach to handle elections with more candidates. Adaptively weighted averages of test supermartingales are a general tool, useful beyond election audits to test collections of hypotheses sequentially while rigorously controlling the familywise error rate. Comment: 16 pages, 3 figures. Presented at E-Vote-ID 2023. This version contains minor corrections to match the final published version
Keywords	Statistics - Applications ; Computer Science - Cryptography and Security ; Computer Science - Computers and Society ; Statistics - Methodology
Publishing date	2023-07-20
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Randomization tests in clinical trials with multiple imputation for handling missing data.

Ivanova, Anastasia / Lederman, Seth / Stark, Philip B / Sullivan, Gregory / Vaughn, Ben

Journal of biopharmaceutical statistics

2022 Volume 32, Issue 3, Page(s) 441–449

Abstract: Randomization-based inference is a useful alternative to traditional population model-based methods. In trials with missing data, multiple imputation is often used. We describe how to construct a randomization test in clinical trials where multiple ... ...

Abstract	Randomization-based inference is a useful alternative to traditional population model-based methods. In trials with missing data, multiple imputation is often used. We describe how to construct a randomization test in clinical trials where multiple imputation is used for handling missing data. We illustrate the proposed methodology using Fisher's combining function applied to individual scores in two post-traumatic stress disorder trials.
MeSH term(s)	Data Interpretation, Statistical ; Humans ; Random Allocation
Language	English
Publishing date	2022-06-06
Publishing country	England
Document type	Journal Article
ZDB-ID	1131763-2
ISSN	1520-5711 ; 1054-3406
ISSN (online)	1520-5711
ISSN	1054-3406
DOI	10.1080/10543406.2022.2080695
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Article: EpiChange. Statistiche al tempo della crisi.

Saltelli, Andrea / Stark, Philip B

Epidemiologia e prevenzione

2017 Volume 41, Issue 3-4, Page(s) 165–169

Abstract: Science lies nowadays in the centre of several storms. The better known is the finding of non-reproducibility of many scientific results, which stretches from the medical field (clinic and pre-clinic tests) to study on behaviour (priming research). ... ...

Title translation	Statistics at the time of the crisis.
Abstract	Science lies nowadays in the centre of several storms. The better known is the finding of non-reproducibility of many scientific results, which stretches from the medical field (clinic and pre-clinic tests) to study on behaviour (priming research). Although the bad use of statistics is reported to be a patent cause of the reproducibility crisis, its deep reasons are to be sought elsewhere; particularly, in the passage from a regimen of little science - regulated by small communities of researchers - to the current big science - identified by a hypertrophic production of millions of research papers and by the imperative "publish or perish", in a setting dominated by market. While spirited debates (on vaccines, climate change, GMO) unfold in society, scientific articles which are bought or withdrawn are the signal of a deep crisis not only of science, but also of the expert thought. In this background, statistics is the main defendant, charged with using methods which experts themselves are not able to explain in an understandable way (p-test). Is there an escape? Yes, there is. Researchers can either court the power and defend the status quo, or contribute to a deep process of reformation, refusing both a vision of science as a religion and the idea that the problem is the poor scientific knowledge of the lay public.
MeSH term(s)	Reproducibility of Results ; Statistics as Topic/standards
Language	Italian
Publishing date	2017-05
Publishing country	Italy
Document type	Journal Article
ZDB-ID	1038112-0
ISSN	1120-9763
ISSN	1120-9763
DOI	10.19191/EP17.3-4.P165.048
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