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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Beyond genomics: understanding exposotypes through metabolomics
Rattray, N. J. W., Deziel, N. C., Wallach, J. D., Khan, S. A., Vasiliou, V., Ioannidis, J. P. A., & Johnson, C. H. (2018) · Human Genomics · doi:10.1186/s40246-018-0134-x · Cited by 121
“Poor application has contributed to the irreproducible nature of many studies, so much that the American Statistical Association felt moved to release a statement highlighting six underlying principles to dictate the proper use and interpretation of the p value [92, 93].”
BackgroundExosomal miR‐95‐5p regulates chondrogenesis and cartilage degradation via histone deacetylase 2/8
Mao, G., Hu, S., Zhang, Z., Wu, P., Zhao, X., Lin, R., Liao, W., & Kang, Y. Q. (2018) · Journal of Cellular and Molecular Medicine · doi:10.1111/jcmm.13808 · Cited by 116
“05 was considered statistically significant.(31) All analyses were performed using SPSS Version 20 (IBM Corporation, Armonk, NY, USA).”
MethodWallace, M. L., Mentch, L., Wheeler, B. J., Tapia, A. L., Richards, M., Zhou, S., Yi, L., Redline, S. S., & Buysse, D. J. (2023) · BMC Medical Research Methodology · doi:10.1186/s12874-023-01965-x · Cited by 137
Barreto, A. D., Ford, G. A., Shen, L. F., et al. (2017) · Stroke · doi:10.1161/strokeaha.117.016720 · Cited by 116
“Congestive heart failure 6 (21) 1 (3) 6 (19)”
To curb research misreporting, replace significance and confidence by compatibility
Greenland, S., Mansournia, M. A., & Joffe, M. (2022) · Preventive Medicine · doi:10.1016/j.ypmed.2022.107127 · Cited by 119
“…Bland, 1996; Sterne and Davey Smith, 2001; Gigerenzer, 2004; Greenland, 2011; Gigerenzer and Marewski, 2015; Goodman, 2016; Greenland et al., 2016; Wasserstein and Lazar, 2016; Amrhein et al., 2017; Greenland, 2017; Amrhein et al., 2019; Calin-Jageman and Cumming, 2019; Greenland, 2019b; McShane…”
Application of the Hierarchical Bootstrap to Multi-Level Data in Neuroscience
Saravanan, V., Berman, G. J., & Sober, S. J. (2019) · doi:10.1101/819334 · Cited by 170
Replicability Crisis in Social Psychology: Looking at the Past to Find New Pathways for the Future
Świątkowski, W., & Dompnier, B. (2017) · International Review of Social Psychology · doi:10.5334/irsp.66 · Cited by 132
“Crucially, however, one should always keep in mind that no single statistical index can ever substitute for well-informed scientific thinking (Gigerenzer & Marewski, 2015; Wasserstein & Lazar, 2016).”
BackgroundWhat is epidemiology? Changing definitions of epidemiology 1978-2017
Frérot, M., Lefebvre, A., Aho, S., Callier, P., Astruc, K., & Aho, S. (2018) · PLoS ONE · doi:10.1371/journal.pone.0208442 · Cited by 115
Exploring Trust in Self-Driving Vehicles Through Text Analysis
Lee, J. D., & Kolodge, K. (2019) · Human Factors The Journal of the Human Factors and Ergonomics Society · doi:10.1177/0018720819872672 · Cited by 114
“The multiple criteria needed to select a model highlights the complexity of model selection that is often masked by the allure of p values as a seemingly objective basis for model selection (Wasserstein & Lazar, 2016).”
BackgroundTesting by Betting: A Strategy for Statistical and Scientific Communication
Shafer, G. R. (2021) · Journal of the Royal Statistical Society Series A (Statistics in Society) · doi:10.1111/rssa.12647 · Cited by 117
A case for beta regression in the natural sciences
Geissinger, E. A., Khoo, C. L. L., Richmond, I. C., Faulkner, S. J. M., & Schneider, D. C. (2022) · Ecosphere · doi:10.1002/ecs2.3940 · Cited by 119
Mamma Mia – A randomized controlled trial of an internet-based intervention for perinatal depression
Haga, S. M., Drozd, F., Lisøy, C., Wentzel-Larsen, T., & Slinning, K. (2019) · Psychological Medicine · doi:10.1017/s0033291718002544 · Cited by 99
The need to show minimum clinically important differences in Alzheimer's disease trials
Liu, K. Y., Schneider, L. S., & Howard, R. (2021) · The Lancet Psychiatry · doi:10.1016/s2215-0366(21)00197-8 · Cited by 113
Ten Simple Rules for Effective Statistical Practice
Kass, R. E., Caffo, B. S., Davidian, M., Meng, X.-L., Yu, B., & Reid, N. (2016) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1004961 · Cited by 113
Everything Matters: The ReproNim Perspective on Reproducible Neuroimaging
Kennedy, D. N., Abraham, S. A., Bates, J. F., Crowley, A., Ghosh, S., Gillespie, T., Goncalves, M., Grethe, J. S., Halchenko, Y. O., Hanke, M., et al. (2019) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2019.00001 · Cited by 113
“At a high level, a premium is put on novel, high-profile publications (in contrast to replications and negative findings) and a specific p-value (typically 0.05) as a proxy for truth has been adopted (Simonsohn et al., 2014; Wasserstein and Lazar, 2016).”
BackgroundInfluentialThe role of hyperparameters in machine learning models and how to tune them
Arnold, C., Biedebach, L., Küpfer, A., & Neunhoeffer, M. (2024) · Political Science Research and Methods · doi:10.1017/psrm.2023.61 · Cited by 128
“Tuning hyperparameters is therefore not a form of “ p-hacking ” (Wasserstein and Lazar, 2016; Gigerenzer, 2018) where researchers try different models until they find the one that generates the desired statistics.”
BackgroundWhat big data can do for treatment in psychiatry
Gillan, C. M., & Whelan, R. (2017) · Current Opinion in Behavioral Sciences · doi:10.1016/j.cobeha.2017.07.003 · Cited by 114
Affective forecasting and travel decision-making: An investigation in times of a pandemic
Karl, M., Köck, F., Ritchie, B. W., & Gauß, J. (2021) · Annals of Tourism Research · doi:10.1016/j.annals.2021.103139 · Cited by 122
Reveal, Don’t Conceal: Transforming Data Visualization to Improve Transparency
Weissgerber, T. L., Winham, S. J., Heinzen, E. P., Milin-Lazovic, J. S., Garcia-Valencia, O., Bukumiric, Z., Savic, M. D., Garovic, V. D., & Milic, N. M. (2019) · Circulation · doi:10.1161/circulationaha.118.037777 · Cited by 101
the Japan Environment and Children’s Study Group, Nakayama, S. F., Iwai‐Shimada, M., et al. (2019) · Journal of Exposure Science & Environmental Epidemiology · doi:10.1038/s41370-019-0139-0 · Cited by 109
Solari, S., Egüen, M., Polo, M. J., & Losada, M. Á. (2017) · Water Resources Research · doi:10.1002/2016wr019426 · Cited by 114
“…be equal to or more extreme than the observed value, under a specified statistical model: ‘‘the smaller the p-values, the greater the statistical incompatibility of the data with the null hypothesis, if the underlaying assumption used to calculate the p-values holds’’ [Wasserstein and Lazar, 2016].”
BackgroundLimitations of Randomized Clinical Trials
Kostis, J. B., & Dobrzynski, J. M. (2020) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2020.05.011 · Cited by 116
Lawlor, E. R., Islam, N., Bates, S., Griffin, S. J., Hill, A. J., Hughes, C. A., Sharp, S. J., & Ahern, A. L. (2020) · Obesity Reviews · doi:10.1111/obr.13013 · Cited by 103
Comparing Sentinel-1 and -2 Data and Indices for Agricultural Land Use Monitoring
Holtgrave, A.-K., Röder, N., Ackermann, A., Erasmi, S., & Kleinschmit, B. (2020) · Remote Sensing · doi:10.3390/rs12182919 · Cited by 116
Supply Chain Integrity: A Key to Sustainable Supply Chain Management
Castillo, V. E., Mollenkopf, D. A., Bell, J. E., & Bozdogan, H. (2018) · Journal of Business Logistics · doi:10.1111/jbl.12176 · Cited by 113
“Second, using an EN logistic regression and ICOMP model evaluation criterion, this research uncovers and empirically demonstrates at an exploratory level that both Structural and Moral”
BackgroundMethodInfluentialSmoking and COVID-19: Adding Fuel to the Flame
Kashyap, V. K., Dhasmana, A., Massey, A. E., Kotnala, S., Zafar, N., Jaggi, M., Yallapu, M. M., & Chauhan, S. C. (2020) · International Journal of Molecular Sciences · doi:10.3390/ijms21186581 · Cited by 108
Bendtsen, M. (2018) · Journal of Medical Internet Research · doi:10.2196/10873 · Cited by 105
“Voices have been raised over the past few years against the use and misuse of P values, perhaps most notably in a formal statement from the American Statistical Association clarifying widely agreed-upon principles underlying the proper use of P values [4], the banning of P values from the journal Basic and Applied Soc…”
BackgroundFinan, P. H., Quartana, P. J., Remeniuk, B., Garland, E. L., Rhudy, J. L., Hand, M., Irwin, M. R., & Smith, M. T. (2016) · SLEEP · doi:10.1093/sleep/zsw017 · Cited by 106
Han, H., & Park, J. (2018) · Frontiers in Neuroinformatics · doi:10.3389/fninf.2018.00001 · Cited by 103
Nothing to See Here? A Non‐Inferiority Approach to Parallel Trends
Bilinski, A., & Hatfield, L. A. (2026) · Statistics in Medicine · doi:10.1002/sim.70296 · Cited by 176
Aligning statistical and scientific reasoning
Goodman, S. N. (2016) · Science · doi:10.1126/science.aaf5406 · Cited by 106
Dominick, G. M., Winfree, K. N., Pohlig, R. T., & Papas, M. A. (2016) · JMIR mhealth and uhealth · doi:10.2196/mhealth.6281 · Cited by 102
“Furthermore, as recommended by the American Statistical Association [29], we wanted to draw particular attention to the effect sizes, rather than just relying on P values to demonstrate the magnitude of measurement differences between the devices; currently, GLMM does not provide a way to garner effect sizes.”
Dissecting innovative trend analysis
Serinaldi, F., Chebana, F., & Kilsby, C. G. (2020) · Stochastic Environmental Research and Risk Assessment · doi:10.1007/s00477-020-01797-x · Cited by 105
“Referring to Wasserstein and Lazar (2016), Wasserstein et al. (2019) and references therein for a thorough discussion on misuse and logical flaws of STs, Serinaldi et al. (2018) attempted to warn practicing hydrologists against a mechanistic use of classical trend tests in the analysis of…”
BackgroundMastering the descriptive statistics used in otorhinolaryngology
Laccourreye, O., Jankowski, R., & Lisan, Q. (2020) · European Annals of Otorhinolaryngology Head and Neck Diseases · doi:10.1016/j.anorl.2020.12.004 · Cited by 108
Can we learn lessons from the FDA's approval of aducanumab?
Liu, K. Y., & Howard, R. (2021) · Nature Reviews Neurology · doi:10.1038/s41582-021-00557-x · Cited by 104
Advancing AI in healthcare: A comprehensive review of best practices
Polevikov, S. (2023) · Clinica Chimica Acta · doi:10.1016/j.cca.2023.117519 · Cited by 121
Unfolding adsorption on metal nanoparticles: Connecting stability with catalysis
Dean, J., Taylor, M. G., & Mpourmpakis, G. (2019) · Science Advances · doi:10.1126/sciadv.aax5101 · Cited by 103
King, E. A., Richter, C., Daniels, K., Franklyn-Miller, A., Falvey, É. C., Myer, G. D., Jackson, M., Moran, R., & Strike, S. C. (2021) · The American Journal of Sports Medicine · doi:10.1177/0363546520985283 · Cited by 103
Frontline physician burnout during the COVID-19 pandemic: national survey findings
Melnikow, J., Padovani, A., & Miller, M. (2022) · BMC Health Services Research · doi:10.1186/s12913-022-07728-6 · Cited by 101
“Lastly, estimates of differences not showing statistical significance should not be interpreted as true zeros without further investigation, as emphasized by the American Statistical Association in 2016 [28, 29].”
BackgroundThe Dozen Things Experimental Economists Should Do (More of)
Czibor, E., Jimenez‐Gomez, D., & List, J. A. (2019) · Southern Economic Journal · doi:10.1002/soej.12392 · Cited by 102
“The outcome of this comparison is summarized in the p-value: the probability under the specified model that the test statistic would be equal to or more extreme than its observed value (Wasserstein and Lazar, 2016).”
InfluentialThe poolr Package for Combining Independent and Dependent p Values
Cinar, O., & Viechtbauer, W. (2022) · Journal of Statistical Software · doi:10.18637/jss.v101.i01 · Cited by 113
“…multiple SNPs. Finally, we are fully aware of concerns about the overuse, overemphasis, or misinterpretation of p values and potential dangers that may arise from drawing dichotomous conclusions based thereupon (e.g., see Wasserstein and Lazar 2016, and the corresponding supplementary materials).”
BackgroundIsbell, D. R., BROWN, D., Chen, M., Derrick, D. J., Ghanem, R., Arvizu, M. N. G., Schnur, E., Zhang, M., & Plonsky, L. (2022) · Modern Language Journal · doi:10.1111/modl.12760 · Cited by 108
Leek, J. T., McShane, B. B., Gelman, A., Colquhoun, D., Nuijten, M. B., & Goodman, S. N. (2017) · Nature · doi:10.1038/d41586-017-07522-z · Cited by 100
PLS-Based Model Selection: The Role of Alternative Explanations in Information Systems Research
Sharma, P. N., Sarstedt, M., Galit Shmueli, A., Kim, K. H., & Thiele, K. O. (2019) · tub.dok (Hamburg University of Technology) · doi:10.17005/1.jais.00538 · Cited by 98
“…Association has recently cautioned that an overreliance on p-values, and especially the prevalent thresholds (e.g., 1%, 5% significance levels), may cause selective inference and lead researchers to “cherry-pick” promising findings, the so-called “p-value hacking” issue (Wasserstein & Lazar, 2016).”
BackgroundSanchis‐Segura, C., Ibáñez, M. V., Aguirre, N., Cruz-Gómez, Á. J., & Forn, C. (2020) · Scientific Reports · doi:10.1038/s41598-020-69361-9 · Cited by 98
Robbins, T., Gonevski, M., Clark, C. C. T., Baitule, S., Sharma, K., Magar, A., Patel, K., Sankar, S., Kyrou, I., Ali, A., et al. (2021) · Clinical Medicine · doi:10.7861/clinmed.2021-0462 · Cited by 97
Improved accuracy in quantitative laser-induced breakdown spectroscopy using sub-models
Anderson, R. B., Clegg, S. M., Frydenvang, J., et al. (2016) · Spectrochimica Acta Part B Atomic Spectroscopy · doi:10.1016/j.sab.2016.12.002 · Cited by 98
Heading for new shores! Overcoming pitfalls in BCI design
Chavarriaga, R., Fried-Oken, M., Kleih, S., Lotte, F., & Scherer, R. (2017) · Brain-Computer Interfaces · doi:10.1080/2326263x.2016.1263916 · Cited by 97
Sex differences in gray matter volume: how many and how large are they really?
Sanchis‐Segura, C., Ibáñez, M. V., Adrián‐Ventura, J., Aguirre, N., Cruz-Gómez, Á. J., Ávila, C., & Forn, C. (2019) · Biology of Sex Differences · doi:10.1186/s13293-019-0245-7 · Cited by 95
Understanding Statistical Hypothesis Testing: The Logic of Statistical Inference
Emmert‐Streib, F., & Dehmer, M. (2019) · Machine Learning and Knowledge Extraction · doi:10.3390/make1030054 · Cited by 129
Wade, R. G., Burr, N. E., McCauley, G., Bourke, G., & Efthimiou, O. (2020) · Annals of Surgery · doi:10.1097/sla.0000000000004076 · Cited by 108
A Call for a Standardized Approach to Reporting Patient-Reported Outcome Measures
Orr, M. N., Klika, A. K., Gagnier, J. J., Bhandari, M., & Piuzzi, N. S. (2021) · Journal of Bone and Joint Surgery · doi:10.2106/jbjs.21.00030 · Cited by 97
Suarez, E. A., Bateman, B. T., Hernández–Díaz, S., Straub, L., Wisner, K. L., Gray, K. J., Pennell, P. B., Lester, B. M., McDougle, C. J., Zhu, Y., et al. (2022) · JAMA Internal Medicine · doi:10.1001/jamainternmed.2022.4268 · Cited by 99
Dmitriev, P., Gupta, S., Kim, D. W., & Vaz, G. J. (2017) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3097983.3098024 · Cited by 106
“However, as pointed out in [4] [30], p-values have been "commonly misused and misinterpreted".”
BackgroundFreyne, B., Donath, S. M., Germano, S., Gardiner, K., Casalaz, D. M., Robins-Browne, R. M., Amenyogbe, N., Messina, N. L., Netea, M. G., Flanagan, K. L., et al. (2018) · The Journal of Infectious Diseases · doi:10.1093/infdis/jiy069 · Cited by 96
Addressing Methodological Challenges in Culture-Comparative Research
Fischer, R. D., & Poortinga, Y. H. (2018) · Journal of Cross-Cultural Psychology · doi:10.1177/0022022117738086 · Cited by 92
“A finding is considered worth reporting if nominally p ≤ .05, which refers to the probability that, given the chosen statistical model, the statistical summary of the data is equal to or more extreme than the observed data (Wasserstein & Lazar, 2016).”
MethodGestational diabetes and the risk of late stillbirth: a case–control study from England, UK
Stacey, T., Tennant, P. W. G., McCowan, L., Mitchell, E. A., Budd, J., Li, M., Thompson, J., Martin, B. L., Roberts, D., & Heazell, A. E. P. (2019) · BJOG An International Journal of Obstetrics & Gynaecology · doi:10.1111/1471-0528.15659 · Cited by 104
Justify Your Alpha: A Primer on Two Practical Approaches
Maier, M., & Lakens, D. (2022) · Advances in Methods and Practices in Psychological Science · doi:10.1177/25152459221080396 · Cited by 141
“This normative use of statistics was criticized in a statement by the American Statistical Association (Wasserstein & Lazar, 2016), who wrote: “We teach it because it’s what we do; we do it because it’s what we teach” (p. 129).”
MethodReliable change and the reliable change index: still useful after all these years?
Blampied, N. M. (2022) · The Cognitive Behaviour Therapist · doi:10.1017/s1754470x22000484 · Cited by 101
“Their respective reports (ASA: Wasserstein and Lazar, 2016; APA: Wilkinson and Task Force on Statistical Inference, 1999), while not suggesting the abandonment of NHST, made many recommendations for the improvement of research practices involving it, although there is not much evidence that these…”
Computational Models of Retrieval Processes in Sentence Processing
Vasishth, S., Nicenboim, B., Engelmann, F., & Burchert, F. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.09.003 · Cited by 86
Restorative benefits of everyday green exercise: A spatial approach
Kajosaari, A., & Pasanen, T. (2020) · Landscape and Urban Planning · doi:10.1016/j.landurbplan.2020.103978 · Cited by 96
Michel, M. C., Murphy, T. J., & Motulsky, H. J. (2019) · Journal of Pharmacology and Experimental Therapeutics · doi:10.1124/jpet.119.264143 · Cited by 90
Cybulski, Ł., Mayo‐Wilson, E., & Grant, S. (2016) · Journal of Consulting and Clinical Psychology · doi:10.1037/ccp0000115 · Cited by 94
Hengartner, M. P., & Plöderl, M. (2018) · Frontiers in Psychiatry · doi:10.3389/fpsyt.2018.00517 · Cited by 99
An Introduction to Second-Generation p -Values
Blume, J. D., Greevy, R. A., Welty, V. F., Smith, J. R., & Dupont, W. D. (2019) · The American Statistician · doi:10.1080/00031305.2018.1537893 · Cited by 94
“For decades now, the statistical community has discouraged researchers from perseverating on the significance level of new findings (Wasserstein and Lazar 2016).”
BackgroundDusen, B. V., & Nissen, J. (2020) · Physical Review Physics Education Research · doi:10.1103/physrevphyseducres.16.010117 · Cited by 97
Svantesson, E., Senorski, E. H., Webster, K. E., et al. (2020) · Knee Surgery Sports Traumatology Arthroscopy · doi:10.1007/s00167-020-06061-x · Cited by 92
Statistical Inference Enables Bad Science; Statistical Thinking Enables Good Science
Tong, C. (2019) · The American Statistician · doi:10.1080/00031305.2018.1518264 · Cited by 103
“He wrote that because of the doubtfulness of the usual statistical model assumption that patients in a case– control study constitute a random sample, Scientifically, the strength of the case against smoking rests not so much on the P-values, but more on the size of the effect, on its coherence and on extensive replic…”
BackgroundMethodGooßen, K., Becker, M., Marshall, M. R., Bühn, S., Breuing, J., Firanek, C. A., Heß, S., Nariai, H., Sloand, J. A., Yao, Q., et al. (2020) · American Journal of Kidney Diseases · doi:10.1053/j.ajkd.2019.10.004 · Cited by 93
Field effects studies in the Chernobyl Exclusion Zone: Lessons to be learnt
Beresford, N. A., Scott, E. M., & Copplestone, D. (2019) · Journal of Environmental Radioactivity · doi:10.1016/j.jenvrad.2019.01.005 · Cited by 91
“Correct interpretation of statistical results play a key role in ensuring that conclusions are sound and that uncertainty surrounding them is represented properly (Wasserstein and Lazar, 2016).”
BackgroundEditorial: The Minimum Clinically Important Difference—The Least We Can Do
Leopold, S. S., & Porcher, R. (2017) · Clinical Orthopaedics and Related Research · doi:10.1007/s11999-017-5253-5 · Cited by 90
“Bayesian statistics [16], which can help the reader arrive at a more-complete understanding of the data.”
BackgroundCrilly, C. J., Haneuse, S., & Litt, J. S. (2020) · Pediatric Research · doi:10.1038/s41390-020-0968-5 · Cited by 89
Conway, M., Byrd, A. M., & van Eggermond, M. A. B. (2018) · Journal of Transport and Land Use · doi:10.5198/jtlu.2018.1074 · Cited by 87
“When using a 95 percent confidence level, for example, 5 percent of the time we can expect to get a statistically significant result when in the change was not due to the scenario. is problem is exacerbated when many p-values are computed as a part of an analysis (Wasserstein & Lazar, 2016).”
MethodBlood Biomarkers With Parkinson's Disease Clusters and Prognosis: The Oxford Discovery Cohort
Lawton, M., Baig, F., Toulson, G., Morovat, A., Evetts, S. G., Ben‐Shlomo, Y., & Hu, M. T. (2019) · Movement Disorders · doi:10.1002/mds.27888 · Cited by 90
Ukah, U. V., Bayrampour, H., Sabr, Y., Razaz, N., Chan, W., Lim, K. I., & Lisonkova, S. (2019) · PLoS Medicine · doi:10.1371/journal.pmed.1003009 · Cited by 93
“All p-values are reported as recommended by the American Statistical Association [28].”
MethodBradley, S. H., DeVito, N., Lloyd, K. E., Richards, G. C., Rombey, T., Wayant, C., & Gill, P. J. (2020) · Journal of the Royal Society of Medicine · doi:10.1177/0141076820956799 · Cited by 98
Staples, J. M., Neilson, E. C., Bryan, A. E. B., & George, W. H. (2017) · The Journal of Sex Research · doi:10.1080/00224499.2017.1393651 · Cited by 105
“Given the near significance and limitations of p values as a means of evaluating the importance of findings (Wasserstein & Lazar, 2016), the interaction was probed; however, it should be interpreted with caution.”
MethodRethinking control chart design and evaluation
Woodall, W. H., & Faltin, F. W. (2019) · Quality Engineering · doi:10.1080/08982112.2019.1582779 · Cited by 88
“See Wasserstein and Lazar (2016).”
Laccourreye, O., & Maisonneuve, H. (2019) · European Annals of Otorhinolaryngology Head and Neck Diseases · doi:10.1016/j.anorl.2019.09.002 · Cited by 101
Shatz, I. (2023) · Behavior Research Methods · doi:10.3758/s13428-023-02072-x · Cited by 145
“…works (Anderson et al., 2001; Bilon, 2021; Cumming, 2014; Di Leo & Sardanelli, 2020; Ernst & Albers, 2017; Gelman & Stern, 2006; Knief & Forstmeier, 2021; Kozak & Piepho, 2018; Lakens, 2021; Rosnow & Rosenthal, 1989; Tijmstra, 2018; Wasserstein & Lazar, 2016; Winter, 2019; Zuur et al., 2010).”
BackgroundResultInfluentialMicroeconometric dynamic panel data methods: Model specification and selection issues
Kiviet, J. F. (2019) · Econometrics and Statistics · doi:10.1016/j.ecosta.2019.08.003 · Cited by 86
“In a recent editorial statement of the American Statistical Association Wasserstein and Lazar (2016) clarify some principles regarding p-values, all the time just considering situations in which the second shortcoming is absent.”
BackgroundLassa fever outcomes and prognostic factors in Nigeria (LASCOPE): a prospective cohort study
Duvignaud, A., Jaspard, M., Etafo, I. C., Gabillard, D., Serra, B., Abejegah, C., Gal, C. L., Abidoye, A. T., Doutchi, M., Owhin, S. O., et al. (2021) · The Lancet Global Health · doi:10.1016/s2214-109x(20)30518-0 · Cited by 91
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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