The DOI matches its Crossref record; 50 of the 50 references it cites were found in a registry or an index; none of the 1 sources its claims cite is checked for support yet.
The ASA Statement on p -Values: Context, Process, and Purpose
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24 Own interpretation · 2 No basis given · 1 No full text of the sources
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The widespread use of”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Statistical significance is not equivalent to scientific, human, or economic significance.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“For example, a p-value near 0.05 taken by itself offers only weak evidence against the null hypothesis.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“By itself, a p-value does not provide a good measure of evidence regarding a model or hypothesis.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Similarly, identical estimated effects will have different p-values if the precision of the estimates differs.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A p-value provides one approach to summarizing the incompatibility between a particular set of data and a proposed model for the data.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 1. P-values can indicate how incompatible the data are with a specified statistical model.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Conducting multiple analyses of the data and reporting only those with certain p-values (typically those passing a significance threshold) renders the reported p-values essentially uninterpretable.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: unclear — It is unclear why the paper says it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Whenever a researcher chooses what to present based on statistical results, valid interpretation of those results is severely compromised if the reader is not informed of the choice and its basis.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.
boosteddirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The smaller the p-value, the greater the statistical incompatibility of the data with the null hypothesis, if the underlying assumptions used to calculate the p-value hold.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 1. P-values can indicate how incompatible the data are with a specified statistical model.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Valid scientific conclusions based on p-values and related statistics cannot be drawn without at least knowing how many and which analyses were conducted, and how those analyses (including p-values) were selected for reporting.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.
plainconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
- Probably not a substantial claim. 10
- It may state two separate claims as one. 10
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“This has led to some scientific journals discouraging the use of p-values, and some scientists and statisticians recommending their abandonment, with some arguments essentially unchanged since p-values were first introduced.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 1. Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
The paper states this without a source or data behind it here, so there is nothing to check it against. The quote shows it is in the paper.
In the paper
“The statistical community has been deeply concerned about issues of reproducibility and replicability of scientific conclusions.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > Abstract
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“While the p-value can be a useful statistical measure, it is commonly misused and misinterpreted.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 1. Introduction
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“A conclusion does not immediately become”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“All these measures and approaches rely on further assumptions, but they may more directly address the size of an effect (and its associated uncertainty) or whether the hypothesis is correct.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 4. Other Approaches
hedgedconditional- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“The validity of scientific conclusions, including their reproducibility, depends on more than the statistical methods themselves.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 1. Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. The paper may give no reason why it holds.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“It is a statement about data in relation to a specified hypothetical explanation, and is not a statement about the explanation itself.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 2. P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Researchers should recognize that a p-value without context or other evidence provides limited information.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Likewise, a relatively large p-value does not imply evidence in favor of the null hypothesis; many other hypotheses may be equally or more consistent with the observed data.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“This incompatibility can be interpreted as casting doubt on or providing evidence against the null hypothesis or the underlying assumptions.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 1. P-values can indicate how incompatible the data are with a specified statistical model.
plaincorrelational- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Appropriately chosen techniques, properly conducted analyses and correct interpretation of statistical results also play a key role in ensuring that conclusions are sound and that uncertainty surrounding them is represented properly.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 1. Introduction
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: fails — The paper may give no reason why it holds.
Probably not a substantial claim. It may be too vague to check against evidence. The paper’s conclusions may not depend on it. The paper may give no reason why it holds. It may state two separate claims as one.
In the paper
“Misunderstanding or misuse of statistical inference is only one cause of the”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > Abstract
What the sources say
- Not checked — no full text of the sourcecites Peng 2015 source
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: unclear — It is unclear whether the paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Smaller p-values do not necessarily imply the presence of larger or more important effects, and larger p-values do not imply a lack of importance or even lack of effect.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 3. Scientific conclusions and business or policy decisions should not be based only on whether a p-value passes a specific threshold.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 2. P-values do not measure the probability that the studied hypothesis is true, or the probability that the data were produced by random chance alone.
plain- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: passes — The paper’s conclusions depend on it.
- Based: passes — The paper gives a reason why it holds.
It may state two separate claims as one.
A conclusion the paper draws from its own results — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“In view of the prevalent misuses of and misconceptions concerning p-values, some statisticians prefer to supplement or even replace p-values with other approaches.”
§ The ASA's Statement on p-Values: Context, Process, and Purpose In > 4. Other Approaches
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: passes — It can be checked against evidence.
- Load-bearing: fails — The paper’s conclusions may not depend on it.
- Based: passes — The paper gives a reason why it holds.
Probably not a substantial claim. The paper’s conclusions may not depend on it.
The paper relies on this without support.
There is widespread consensus in the statistical community on the principles articulated in the ASA statement.
“according to widespread consensus in the statistical community”
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“While the large scale of the Tabula Muris Senis dataset provides substantial power to predict subtle changes in LR interactions, the biological complexity, dependencies among communication pairs, and multiple testing burden require that these p-values be interpreted within a broader biological and statistical context [33].”
From Quantitative profiling of lifespan-dependent cell-cell communication potential reveals dynamic ligand-receptor network shifts across mouse tissues (2026) · cites this paper as [33]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“These types of incorrect descriptions contribute to confirmation bias, overinterpretation of results, and reporting correlation as implied causation (Wasserstein and Lazar 2016).”
From Unethical Numbers? A Meta-analysis of Library Learning Analytics Studies (2019) · cites this paper as Wasserstein and Lazar 2016
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“This is especially true of relatively large-sample-size studies (as most learning analytics studies are), which are likely to obtain statistically significant findings in relatively small variations in measures such as GPA (Field 2017; Salkind 2016 Wasserstein and Lazar 2016).”
From Unethical Numbers? A Meta-analysis of Library Learning Analytics Studies (2019) · cites this paper as Wasserstein and Lazar 2016
In the citing paper
“In 2016, the American Statistical Association released a statement on statistical significance and the P value with six principles underlying the proper use and interpretation of this statistical tool (77).”
From Misrepresentation and distortion of research in biomedical literature (2018) · cites this paper as [77]
In the citing paper
“Although we also present p values for exploratory analyses, we refrain from labeling any of them as statistically significant, following best-practice recommendations for statistical analysis [43].”
From Comparing quality of reporting between preprints and peer-reviewed articles in the biomedical literature (2020) · cites this paper as [43]
In the citing paper
“Contrary to IJE author recommendations and recent guidance from the American Statistics Society, 16 Gao et al. largely base their conclusions on findings with a P -value equivalent to < 0.05 after multiple testing.”
From Commentary: Two-sample Mendelian randomization: opportunities and challenges (2016) · cites this paper as [16]
In the citing paper
“Following guidance,50 we focused on 95% CIs, rather than statistical significance. For example, unlike conventional interpretations, we did not outright interpret an effect estimate “non‐significant” if the lower or upper bound of the 95% CI was slightly above/below the null value; we interpreted them as “suggestive” of an effect.”
From Third‐wave cognitive behaviour therapies for weight management: A systematic review and network meta‐analysis (2020) · cites this paper as [50]
Citations
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“93 96 Before pro-ceeding, let us ask a few questions.”
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“Notably, we follow recomme ndations from the American Statistical Association (43) and use Bayesian inference in our main analysis (Supplementary Methods), although some p-value hypothesis tests are reported for demographics.”
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BackgroundThe ASA's p-value Statement, One Year on
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Inference and Prediction Diverge in Biomedicine
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“If the scientific goal is to examine whether an effect exists or which specific input variables have most impact on an outcome, nullhypothesis testing legitimately remains the gold standard.(12) However, a few authors, including John Ioannidis, have cast doubt on the notion that computing p values and the accompanying…”
BackgroundLeichtmann, B., Nitsch, V., & Mara, M. (2022) · Frontiers in Robotics and AI · doi:10.3389/frobt.2022.838116 · Cited by 66
“…mindless use of QRPs is the misunderstanding or misuse of Null-Hypothesis Signi fi cance Testing (NHST), as the American Statistical Association emphasized in a statement on p -values (e.g., the confusion of statistical signi fi cance with the importance of a result) (Wasserstein and Lazar, 2016).”
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Making ERP research more transparent: Guidelines for preregistration
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“…provide this information, however, continues to persist in scientific literature, which has led to a vast body of literature criticizing null hypothesis significance testing (NHST), the interpretation of p-values, and the 0.05 significance level (Wasserstein & Lazar 2016; Wasserstein et al. 2019).”
BackgroundInfluentialA review of some emergent quantitative analyses in sport and exercise psychology
Myers, N. D., Ntoumanis, N., Gunnell, K. E., Gucciardi, D. F., & Lee, S. (2017) · International Review of Sport and Exercise Psychology · doi:10.1080/1750984x.2017.1317356 · Cited by 71
“While the utility of null hypothesis significance testing (NHST) has been debated in statistics (e.g., Wasserstein & Lazar, 2016), psychology (e.g., Cohen, 1994) and exercise science (e.g., Zhu, 2012), ‘NHST is still the engine of statistical inference in most health and exercise sciences’…”
BackgroundMoote, J., Archer, L., DeWitt, J., & MacLeod, E. (2019) · Journal of Engineering Education · doi:10.1002/jee.20302 · Cited by 67
“We additionally focused on the interpretation of effect sizes rather than p values to reduce the risk of Type 1 errors and provide insight into the practical implications of the magnitude of any reported differences (Sun, Pan, & Wang, 2010; Wasserstein & Lazar, 2016).”
MethodWilliams, R. S., Hegglin, M. I., Kerridge, B. J., Jöckel, P., Latter, B. G., & Plummer, D. A. (2019) · Atmospheric Chemistry and Physics · doi:10.5194/acp-19-3589-2019 · Cited by 66
A framework for learning from null results
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“DECEmbER 2019 583 that an exclusive focus on the statistical significance of effects without considering the substantive magnitude of those effects is misplaced (e.g., Wasserstein & Lazar, 2016).”
BackgroundEvidence-Based Medicine and Clinical Research: Both Are Needed, Neither Is Perfect
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The Impact of Education and Age on Metabolic Disorders
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The Role of p -Values in Judging the Strength of Evidence and Realistic Replication Expectations
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Interrater reliability estimators tested against true interrater reliabilities
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Contribution of Particulates to Airborne Disease Transmission and Severity: A Review
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Crisis or self-correction: Rethinking media narratives about the well-being of science
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“And, in 2016, the American Statistical Association published principles “underlying the proper use and interpretation of the p-value” (59).”
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“The ability to quantify evidence for both the null and the alternative is a key benefit of Bayesian methods over null hypothesis significance testing.. 36,37 A corresponding way of quantifying the evidence of an effect is via Bayes factors (see InfoBox 1 for more detail).”
MethodEffect of acupuncture and intervention types on weight loss: a systematic review and meta‐analysis
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“Importantly, P value can be strongly affected by sample size (32).”
BackgroundAccurate case‐outcome modeling in economics, psychology, and marketing
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The Critique of Empirical Social Science: New Policies atManagement and Organization Review
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“…Statistical Association recently declared: ‘Practices that reduce data analysis or scientific inference to mechanical “bright-line” rules (such as p 0.05) for justifying scientific claims or conclusions can lead to erroneous beliefs and poor decision making’ (Wasserstein & Lazar, 2016: 131).”
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Extended and updated tables for the Friedman rank test
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“Recently Wasserstein and Lazar (2016) discussed about the principles underlying the proper p-values usage which will not be analyzed further here.”
BackgroundModeling and Optimization Approaches of Laser-Based Powder-Bed Fusion Process for Ti-6Al-4V Alloy
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“Hence, the p-value is the probability of observing test output values at least as high as the values obtained during the actual test, while the so-called “null hypothesis” is presumed to be correct [60].”
BackgroundRached, N. A., Kley, S., Störck, M., Meyer, T., & Stücker, M. (2023) · Journal of Clinical Medicine · doi:10.3390/jcm12155121 · Cited by 70
“Addressing model assumptions, including appropriate correlations between measurements on the same patient and homoscedasticity of residuals, is important for optimal model fit and honest inference [19], as emphasized in the 2016 American Statistical Association statement [21,22].”
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“Results were considered ‘significant’ when P 0.05 (but cf. Nuzzo, 2014, Wasserstein & Lazar, 2016).”
InfluentialBayesian Data Analysis in Empirical Software Engineering Research
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“Unfortunately, this widely used approach to testing hypotheses suffers from serious shortcomings [22], which have prompted calls to seriously reconsider how it’s used as a statistical practice [11], [97], or even to abandon it alto-gether [2], [63].”
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DEMoS: an Italian emotional speech corpus
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Sex differences in systemic metabolites at four life stages: cohort study with repeated metabolomics
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The financial impact of carbon risk and mitigation strategies: Insights from the automotive industry
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Analyzing Therapeutic Change Using Modified Brinley Plots: History, Construction, and Interpretation
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Synergistic impacts of habitat loss and fragmentation on model ecosystems
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How Powerful is the Evidence in Criminology? On Whether We Should Fear a Coming Crisis of Confidence
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“The compatibility between the observed data and H0 is summarized with a P value, where smaller P values (i.e., those closer to 0.00) indicate the data and H0 are less compatible (Wasserstein & Lazar, 2016).”
MethodInfluentialAn Updated Survey on Statistical Thresholding and Sample Size of fMRI Studies
Yeung, A. W. K. (2018) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2018.00016 · Cited by 60
“Regardless of the theoretical methods used, the effect sizes should be reported alongside the brain maps of p-values for better comprehension of the results (Wasserstein and Lazar, 2016).”
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“We chose this approach, rather than conducting hypothesis testing and calculating p value estimates, because of the widespread misinterpretation of the latter.(31,32) While we acknowledge that the interpretation of confidence intervals, like the interpretation of p values, requires assumptions about data generation pr…”
Anemia in tuberculosis cases: A biomarker of severity?
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“Due to critical use of the p-value, we added 95% confidence intervals (CIs) to the estimates [22].”
MethodIn praise of Table 1: The importance of making better use of descriptive statistics
Murphy, K. R. (2021) · Industrial and Organizational Psychology · doi:10.1017/iop.2021.90 · Cited by 63
“…across a wide range of disciplines are rising up against the uncritical use of significance tests as tools for evaluating research results (Amrhein et al., 2019; Wasserstein & Lazar, 2016; Wasserstein et al., 2019), organizational scientists are increasing their reliance on these dubious tools.”
BackgroundLukan, J., Bolliger, L., Pauwels, N., Luštrek, M., Bacquer, D. D., & Clays, E. (2022) · BMC Public Health · doi:10.1186/s12889-021-12354-8 · Cited by 61
“While a fixation on statistical significance has been widely criticised [47], it served as a good first step in comparisons of heterogeneous studies.”
BackgroundAn Evaluation of Cosmological Models from the Expansion and Growth of Structure Measurements
Zhai, Z., Blanton, M., Slosar, A., & Tinker, J. (2017) · The Astrophysical Journal · doi:10.3847/1538-4357/aa9888 · Cited by 67
“We should emphasize here the point made in Wasserstein & Lazar (2016) that whereas p-values can indicate how incompatible the data are with a specified statistical model, they do not measure the probability that the model is true or the 4 The Galileon model with tracker solution is ruled out by the…”
BackgroundMethodJones, H. E., Kaltenbach, K. A., Benjamin, T. D., Wachman, E. M., & O’Grady, K. E. (2018) · Journal of Addiction Medicine · doi:10.1097/adm.0000000000000463 · Cited by 61
Power Analysis and Sample Size Planning in ANCOVA Designs
Shieh, G. (2019) · Psychometrika · doi:10.1007/s11336-019-09692-3 · Cited by 59
Jerk within the Context of Science and Engineering—A Systematic Review
Hayati, H., Eager, D., Pendrill, A.-M., et al. (2020) · Vibration · doi:10.3390/vibration3040025 · Cited by 59
Hoffmann, S., Schönbrodt, F. D., Elsas, R., Wilson, R., Strasser, U., & Boulesteix, A. (2021) · Royal Society Open Science · doi:10.1098/rsos.201925 · Cited by 71
DiPerna, J. C., Lei, P., Cheng, W., Hart, S. C., & Bellinger, J. M. (2017) · Journal of Educational Psychology · doi:10.1037/edu0000191 · Cited by 62
“…outcomes should not focus exclusively on statistical significance (e.g., Cumming, 2014; Durlak, 2009; Greenwald, Gonzalez, Harris, & Guthrie, 1996; Wasserstein & Lazar, 2016), we also estimated effect sizes of treatment as compared to the control condition based on the previously specified main…”
ResultWhat the P values really tell us
Nahm, F. S. (2017) · The Korean journal of pain · doi:10.3344/kjp.2017.30.4.241 · Cited by 59
“summary would be equal to or more extreme than the actual observed results [2].”
MethodResultMeasurement of Wind Effects on a Kilometer-Level Cable-Stayed Bridge during Typhoon Haikui
Wang, H., Tao, T., Gao, Y., et al. (2018) · Journal of Structural Engineering · doi:10.1061/(asce)st.1943-541x.0002138 · Cited by 59
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“Facilitating a more productive two-way dialogue between the statistical and applied research communities may help to mitigate poor practices [7].”
BackgroundTo Err is Human; To Reproduce Takes Time
Scott, S. L., Gunnoe, T. B., Fornasiero, P., & Crudden, C. M. (2022) · ACS Catalysis · doi:10.1021/acscatal.2c00967 · Cited by 58
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Tracing the horizontal transport of microplastics on rough surfaces
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“We follow the advice of the American Statistical Association and refrain from hypothesis testing [49, 50].”
MethodFarm size affects the use of agroecological practices on organic farms in the United States
Liebert, J. A., Benner, R., Kerr, R. B., Björkman, T., Master, K. T. D., Gennet, S., Gomez, M. I., Hart, A. K., Kremen, C., Power, A. G., et al. (2022) · Nature Plants · doi:10.1038/s41477-022-01191-1 · Cited by 68
Using supply chain databases in academic research: A methodological critique
Culot, G., Podrecca, M., Nassimbeni, G., Orzes, G., & Sartor, M. (2022) · Journal of Supply Chain Management · doi:10.1111/jscm.12294 · Cited by 61
An Introduction to Bayesian Data Analysis for Correlations
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“In recent years, statistical experts have increasingly warned researchers in the medical sciences and other fields about problems stemming from the misuse and misinterpretation of null hypothesis significance testing and P values [1].”
BackgroundAn End-to-End Deep Learning Histochemical Scoring System for Breast Cancer TMA
Liu, J., Xu, B., Zheng, C., Gong, Y., Garibaldi, J. M., Soria, D., Green, A., Ellis, I. O., Zou, W., & Qiu, G. (2018) · IEEE Transactions on Medical Imaging · doi:10.1109/tmi.2018.2868333 · Cited by 62
“001, which means the correlation is considered significant and there is strong evidence against the null hypothesis that there is no relationship between pathologists scores and predicted scores [43].”
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Acculturative stress, emotion regulation, and affective symptomology among Latino/a college students
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“Due to the flaws of null-hypothesis significance testing (Cumming, 2014), confidence intervals, rather than p values, are reported (Wasserstein & Lazar, 2016).”
MethodEffects of pressure and residence time on limonene production in waste tires pyrolysis process
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Hyperspectral imaging and robust statistics in non-melanoma skin cancer analysis
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Optimizing significance testing of astronomical forcing in cyclostratigraphy
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A Primer on Bayesian Analysis for Experimental Psychopathologists
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“However, as noted in a recent statement by the American Statistical Association (ASA), “While the p -value can be a useful statistical measure, it is commonly misused and misinterpreted” (Wasserstein & Lazar, 2016, p. 131).”
Genotypic variability enhances the reproducibility of an ecological study
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Hypothesis Tests for Principal Component Analysis When Variables are Standardized
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“P values were the parameters used to determine the results of the hypothesis test [45].”
Personality and Performance in Specific Neurocognitive Domains Among Older Persons
Chapman, B. P., Benedict, R. H. B., Lin, F. V., Roy, S., Federoff, H. J., & Mapstone, M. E. (2017) · American Journal of Geriatric Psychiatry · doi:10.1016/j.jagp.2017.03.006 · Cited by 55
Metadata
- Authors
- Wasserstein, R. L., & Lazar, N. A.
- Affiliations
- American Statistical Association, 732 North Washington Street, Alexandria, VA 22314-1943
- American Statistical Association (US)
- ORCID
- Lazar, Nicole A.
- Venue
- The American Statistician
- Publisher
- Informa UK Limited
- Published
- 2016-04-02
- Volume
- 70
- Issue
- 2
- Pages
- 129-133
- ISSN
- 0003-1305 (ISSN-L)
- 1537-2731
- OpenAlex
- W2293040502
- Semantic Scholar
- 81a65f23474ffceb2481836328608102950da979
- Cited by
- 6,306
- References
- 45
- Citations by year
- 2026: 625 · 2025: 629 · 2024: 575 · 2023: 543 · 2022: 642 · 2021: 676 · 2020: 678 · 2019: 666 · 2018: 596 · 2017: 438 · 2016: 226
- FWCI
- 649.74
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- The American Statistician (publishedVersion)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Bayesian Inference
- Physical Sciences › Mathematics › Statistics and Probability › Statistics Education and Methodologies
- Physical Sciences › Mathematics › Statistics and Probability › Probability and Statistical Research
- Keywords
- p-values, statistical evidence, p-value interpretation, statistical inference, hypothesis testing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
A language model drew the claims from the paper on 2026-09-28, under version 3 of the claim definition (prompt extract-v3).
A second model labelled each claim on 2026-09-28 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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