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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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- Leek, J. (2014), “On the Scalability of Statistical Procedures: Why the p-Value Bashers Just Don't Get It,” Simply Statistics Blog, Available at http://simplystatistics.org/2014/02/14/on-the-scalability-of-statistical-procedures-why-the-p-value-bashers-just-dont-get-it/.
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Be the Data: Embodied Visual Analytics
Chen, X., Self, J. Z., House, L., Wenskovitch, J. E., Sun, M., Wycoff, N., Evia, J. R., Leman, S. C., & North, C. L. (2017) · IEEE Transactions on Learning Technologies · doi:10.1109/tlt.2017.2757481 · Cited by 23
“There has been some debate over the use of classical methods which rely on p-values to make inference [37].”
MethodResponse to the ASA’s Statement on p-Values: Context, Process, and Purpose
Ionides, E. L., Giessing, A., Ritov, Y., & Page, S. E. (2017) · The American Statistician · doi:10.1080/00031305.2016.1234977 · Cited by 23
Comparative Effectiveness of Enoxaparin vs Dalteparin for Thromboprophylaxis After Traumatic Injury
Miano, T. A., Cuker, A. C., Christie, J. D., Martin, N. D., Smith, B. P., Makley, A. T., Guo, W., & Hennessy, S. (2017) · CHEST Journal · doi:10.1016/j.chest.2017.08.008 · Cited by 24
How Many Times Should One Run a Computational Simulation?
Seri, R., & Secchi, D. (2017) · Understanding complex systems · doi:10.1007/978-3-319-66948-9_11 · Cited by 27
Real-time Driver Identification using Vehicular Big Data and Deep Learning
Jeong, D., Kim, M.-S., Kim, K., Kim, T.-W., Jin, J., Lee, C., & Lim, S. (2018) · IEEE International Conference on Intelligent Transportation Systems (ITSC) · doi:10.1109/itsc.2018.8569452 · Cited by 26
“Using ANOVA, we discovered sensors with low P-values, whereby 10 sensors with the lowest P-values were used for testing [15].”
MethodBig Data Research in Pediatric Neurosurgery: Content, Statistical Output, and Bibliometric Analysis
Oravec, C. S., Motiwala, M., Reed, K., Jones, T. L., & Klimo, P. (2019) · Pediatric Neurosurgery · doi:10.1159/000495790 · Cited by 24
(Re)Validating Cognitive Introductory Computing Instruments
Bockmon, R., Cooper, S., Gratch, J. S., & Dorodchi, M. (2019) · ACM Technical Symposium on Computer Science Education (SIGCSE) · doi:10.1145/3287324.3287372 · Cited by 24
Ryan, E., Harrison, E. M., Pearse, R. M., & Gates, S. (2019) · BMJ Open · doi:10.1136/bmjopen-2018-024256 · Cited by 24
How Large Are Your G -Values? Try Gosset’s Guinnessometrics When a Little “ p ” Is Not Enough
Ziliak, S. T. (2019) · The American Statistician · doi:10.1080/00031305.2018.1514325 · Cited by 23
“…significance; in sum, close study of this giant’s methods can help to undo the crisis of validity currently haunting science and society, including business and law (Goodman 2002; Ziliak and McCloskey 2008; Greenland, et al. 2016; Wasserstein and Lazar, 2016; Pierson, Broman, et al. 2017).”
BackgroundEffects of racism on the socio-emotional wellbeing of Aboriginal Australian children
Macedo, D. M., Smithers, L. G., Roberts, R., Paradies, Y., & Jamieson, L. (2019) · International Journal for Equity in Health · doi:10.1186/s12939-019-1036-9 · Cited by 23
Kumar, A. D., Aikens, R. C., Hom, J., Shieh, L. Y., Chiang, J., Morales, D., Saini, D., Musen, M. A., Baiocchi, M., Altman, R. B., et al. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa190 · Cited by 24
Kelter, R. (2021) · BMC Medical Research Methodology · doi:10.1186/s12874-021-01341-7 · Cited by 25
“The often inadequate use of null hypothesis significance testing (NHST) has been debated widely [3, 4], and the consequences pose severe problems for scientific progress.”
BackgroundFavero, M., Carlucci, S., Chinazzo, G., Møller, J. K., Schweiker, M., Vellei, M., & Sonta, A. J. (2024) · Building and Environment · doi:10.1016/j.buildenv.2024.111903 · Cited by 22
Méndez‐Alonzo, R., Ewers, F. W., Jacobsen, A. L., et al. (2018) · Trees · doi:10.1007/s00468-018-1796-7 · Cited by 23
The p‐value and model specification in statistics
Wang, B., Zhou, Z., Wang, H., et al. (2019) · General Psychiatry · doi:10.1136/gpsych-2019-100081 · Cited by 23
Zhou, L., Kwoh, C. K., Ran, D., et al. (2019) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2019.08.008 · Cited by 23
Medications and Suicide: High Dimensional Empirical Bayes Screening (iDEAS)
Gibbons, R., Hur, K., Lavigne, J. E., Wang, J., & Mann, J. J. (2019) · Harvard Data Science Review · doi:10.1162/99608f92.6fdaa9de · Cited by 23
Hohenfeld, C., Kuhn, H., Müller, C., Nellessen, N. D., Ketteler, S. T., Heinecke, A., Goebel, R., Shah, N. J., Schulz, J. B., Reske, M., et al. (2019) · Behavioural Brain Research · doi:10.1016/j.bbr.2019.112435 · Cited by 24
“The commonly used threshold of p .05 was used merely as an orientation, but not as a hard cut-o ff due to the numerous problems associated with such an approach [for an extensive discussion see [24], and the related works].”
MethodBendtsen, M. (2019) · Journal of Medical Internet Research · doi:10.2196/14420 · Cited by 22
Bromfield, S. G., Sullivan, S. M., Saelee, R., Elon, L., Lima, B. B., Young, A., Uphoff, I., Li, L., Quyyumi, A. A., Bremner, J. D., et al. (2020) · Annals of Behavioral Medicine · doi:10.1093/abm/kaaa015 · Cited by 24
Biosa, G., Giurghita, D., Alladio, E., Vincenti, M., & Neocleous, T. (2020) · Frontiers in Chemistry · doi:10.3389/fchem.2020.00738 · Cited by 24
“Moreover, a very small difference in the calculated p-value produces a completely different interpretation of the obtained results, thus leading to possible severe consequences for the subjects under investigation (Wasserstein and Lazar, 2016).”
Demonstrating equivalence and non‐inferiority of medical education concepts
Klasen, M., & Sopka, S. (2020) · Medical Education · doi:10.1111/medu.14420 · Cited by 25
Bush, H., Stanek, J. M., Wooldridge, J. D., et al. (2020) · Journal of Sport Rehabilitation · doi:10.1123/jsr.2019-0397 · Cited by 23
Investigating Motivational and Cognitive Factors which Impact the Success of Engineering Students
Eichler, A., & Gradwohl, J. (2021) · International Journal of Research in Undergraduate Mathematics Education · doi:10.1007/s40753-020-00127-4 · Cited by 23
Marine ecosystem indicators are sensitive to ecosystem boundaries and spatial scale
Heim, K. C., Thorne, L. H., Warren, J. D., Link, J. S., & Nye, J. A. (2021) · Ecological Indicators · doi:10.1016/j.ecolind.2021.107522 · Cited by 27
“Second, we acknowledge the inadequacy of the null hypothesis-testing framework to examine statistical significance of an assumed monotonic trend (Wasserstein and Lazar, 2016; Hardison et al., 2019).”
BackgroundAnthracyclins Increase PUFAs: Potential Implications in ER Stress and Cell Death
Balgoma, D., Kullenberg, F., Calitz, C., Kopsida, M., Heindryckx, F., Lennernäs, H., & Hedeland, M. (2021) · Cells · doi:10.3390/cells10051163 · Cited by 22
Gillette, D. P., Edds, D. R., Jha, B. R., & Mishra, B. (2021) · Biological Conservation · doi:10.1016/j.biocon.2021.109427 · Cited by 24
Reverse‐Bayes methods for evidence assessment and research synthesis
Held, L., Matthews, R., Ott, M., & Pawel, S. (2021) · Research Synthesis Methods · doi:10.1002/jrsm.1538 · Cited by 28
“Good (1983, p. 29) There is now a common consensus that the most widely-used methods of statistical inference have led to a crisis in both the interpretation of research findings and their replication ( e. g. Gelman and Loken, 2014; Wasserstein and Lazar, 2016).”
BackgroundMethodInfluentialThe Association of Low-Income Housing Tax Credit Units and Reports of Child Abuse and Neglect
Shanahan, M. E., Austin, A. E., Durrance, C. P., Martin, S. L., Mercer, J. A., Runyan, D. K., & Runyan, C. W. (2022) · American Journal of Preventive Medicine · doi:10.1016/j.amepre.2021.11.020 · Cited by 22
Eekhoff, J. D., Abraham, J., Schott, H. R., Solon, L. F., Ulloa, G. E., Zellers, J. A., Cannon, P. C., & Lake, S. P. (2022) · Acta Biomaterialia · doi:10.1016/j.actbio.2022.03.025 · Cited by 23
Kitazume, Y., Kawazoe, H., Uozumi, R., Yoshizawa, T., Iihara, H., Fujii, H., Takahashi, M., Arai, T., Murachi, Y., Sato, Y., et al. (2022) · Scientific Reports · doi:10.1038/s41598-022-10008-2 · Cited by 22
Once an optimist, always an optimist? Studying cognitive judgment bias in mice
Bračić, M., Bohn, L., Siewert, V., von Kortzfleisch, V. T., Schielzeth, H., Kaiser, S., Sachser, N., & Richter, S. H. (2022) · Behavioral Ecology · doi:10.1093/beheco/arac040 · Cited by 22
Sommer, A., Peters, A., Rommel, M., Cyrys, J., Grallert, H., Haller, D., Müller, C. L., & Bind, M. (2022) · PLoS Computational Biology · doi:10.1371/journal.pcbi.1010044 · Cited by 26
“Following the American Statistical Association statement [82, 94], we avoid searching for “statistically significant” results with a dichotomous approach.”
MethodFrequentist Statistical Inference
Wilks, D. S. (2019) · Elsevier eBooks · doi:10.1016/b978-0-12-815823-4.00005-5 · Cited by 23
Poppe, E., Brown, R. A., Recker, J., et al. (2017) · Information Systems · doi:10.1016/j.is.2017.01.004 · Cited by 23
Multiplicity control, school uniforms, and other perplexing debates.
Cribbie, R. A. (2017) · Canadian Journal of Behavioural Science/Revue canadienne des sciences du comportement · doi:10.1037/cbs0000075 · Cited by 25
“The quick turnaround in thinking even prompted the American Statistical Association to release a statement on null hypothesis significance testing (Wasserstein & Lazar, 2016), such that the authors explained what p values can and cannot tell you about your results.”
BackgroundNevatte, T. M., Ward, R. D., Sedda, L., & Hamilton, J. G. C. (2017) · Scientific Reports · doi:10.1038/s41598-017-06313-w · Cited by 23
The escalation dating abuse workshop for college students: Results of an efficacyRCT
Rothman, E. F., Paruk, J. K., & Banyard, V. L. (2018) · Journal of American College Health · doi:10.1080/07448481.2018.1431909 · Cited by 23
“05 cutpoint has been widely discussed.(65) Failing to note potentially promising results, as compared to finding too”
ResultBayesian evaluation of behavior change interventions: a brief introduction and a practical example
Heino, M. T. J., Vuorre, M., & Hankonen, N. E. (2018) · Health Psychology and Behavioral Medicine · doi:10.1080/21642850.2018.1428102 · Cited by 27
“Note how we do not find e.g. the probability of being wrong, or the hypothesis being false, or the probability of getting the same result in a replication study (Gigerenzer, 2004; Wasserstein & Lazar, 2016).”
Sanders‐Pinheiro, H., Colugnati, F. A. B., Marsicano, E. O., Geest, S. D., & Medina, J. O. P. (2018) · BMC Nephrology · doi:10.1186/s12882-018-0840-6 · Cited by 23
Peer-Review Guidelines Promoting Replicability and Transparency in Psychological Science
Davis, W. E., Giner‐Sorolla, R., Lindsay, D. S., Lougheed, J. P., Makel, M. C., Meier, M. E., Sun, J., Vaughn, L. A., & Zelenski, J. M. (2018) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245918806489 · Cited by 24
“…invoke the American Statistical Association’s guideline that p values can be interpreted correctly only with full knowledge of the hypotheses tested (Wasserstein & Lazar, 2016) and note that with exploratory analyses, the focus should be on confidence intervals and effect sizes, rather than p…”
BackgroundSuthana, N., Aghajan, Z. M., Mankin, E. A., & Lin, A. (2018) · Frontiers in Neuroscience · doi:10.3389/fnins.2018.00905 · Cited by 20
Metabolic profiling of adolescent non-alcoholic fatty liver disease
Hartley, A. E., Ferreira, D. L. S., Anderson, E. L., & Lawlor, D. A. (2018) · Wellcome Open Research · doi:10.12688/wellcomeopenres.14974.1 · Cited by 22
Replicability and randomization test logic in behavior analysis
Jacobs, K. W. (2019) · Journal of the Experimental Analysis of Behavior · doi:10.1002/jeab.501 · Cited by 23
Will the ASA's Efforts to Improve Statistical Practice be Successful? Some Evidence to the Contrary
Hubbard, R. (2019) · The American Statistician · doi:10.1080/00031305.2018.1497540 · Cited by 22
“0%) entries written by scholars trained in psychology, the remainder by economists (Leamer 1983; Ziliak and McCloskey 2008), sociologists (Morrison and Henkel 1970), statisticians (Berger and Sellke 1987; Wasserstein and Lazar 2016), and an interdisciplinary mix (Gigerenzer et al.”
BackgroundInfluentialThe Fragility Index—P Values Reimagined, Flaws and All
Acuña, S. A., Sue‐Chue‐Lam, C., & Dossa, F. (2019) · JAMA Surgery · doi:10.1001/jamasurg.2019.0567 · Cited by 23
Spatial confidence sets for raw effect size images
Bowring, A., Telschow, F. J. E., Schwartzman, A., & Nichols, T. E. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116187 · Cited by 22
Quick and accurate attack detection in recommender systems through user attributes
Aktukmak, M., Yılmaz, Y., & Uysal, I. (2019) · ACM Conference on Recommender Systems (RecSys) · doi:10.1145/3298689.3347050 · Cited by 23
Fighting or embracing multiplicity in neuroimaging? neighborhood leverage versus global calibration
Chen, G., Taylor, P. A., Cox, R. W., & Pessoa, L. (2019) · NeuroImage · doi:10.1016/j.neuroimage.2019.116320 · Cited by 24
“Accordingly, the modeling framework resonates with critical assessments of the NHST framework which have intensified in recent years (Nuzzo, 2014; Wasserstein and Lazar, 2016; McShane et al., 2018; Amrhein et al., 2019).”
MethodBalgoma, D., Zelleroth, S., Grönbladh, A., Hallberg, M., Pettersson, C. E., & Hedeland, M. (2020) · Metabolomics · doi:10.1007/s11306-019-1632-0 · Cited by 22
Cremen, G., Werner, M. J., & Baptie, B. J. (2020) · Bulletin of the Seismological Society of America · doi:10.1785/0120190238 · Cited by 24
Syngelaki, E., Schinkel, C. C. F., Klatt, S., & Hörandl, E. (2020) · Frontiers in Plant Science · doi:10.3389/fpls.2020.00435 · Cited by 23
A default prior for regression coefficients
van Zwet, E. W. (2018) · Statistical Methods in Medical Research · doi:10.1177/0962280218817792 · Cited by 21
Justify Your Alpha: A Primer on Two Practical ApproachesPreprint
Maier, M., & Lakens, D. (2021) · doi:10.31234/osf.io/ts4r6 · Cited by 23
Does consumer knowledge about sustainable-fashion impact intention-to-buy?
Han, J., Woodside, A. G., & Ko, E. (2024) · Asia Pacific Journal of Marketing and Logistics · doi:10.1108/apjml-05-2023-0456 · Cited by 23
Cho, P. J., Olaye, I. M., Shandhi, M. M. H., Daza, E. J., Foschini, L., & Dunn, J. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00219-x · Cited by 26
CAUM: A software for calculating and assessing chemical ages of uranium minerals
Song, H., Xiao, T., Chi, G., et al. (2025) · Geoscience Frontiers · doi:10.1016/j.gsf.2025.102031 · Cited by 22
Berry, D. A. (2017) · Journal of the American Statistical Association · doi:10.1080/01621459.2017.1316279 · Cited by 22
Fencl, J. S., Mather, M. E., Smith, J. M., & Hitchman, S. M. (2017) · Ecosphere · doi:10.1002/ecs2.1973 · Cited by 23
The Fragility of Statistically Significant Randomized Controlled Trials in Plastic Surgery
Chin, B. H., Copeland, A. E., Gallo, L., Wakeham, S., Coroneos, C. J., Walsh, M. W., Thoma, A., & Voineskos, S. H. (2019) · Plastic & Reconstructive Surgery · doi:10.1097/prs.0000000000006102 · Cited by 22
Kim, Y. H., Park, C. S., Bae, H., Lim, E. J., Kang, K., Lee, E. S., Jo, S. H., & Huh, M. R. (2020) · Journal of people, plants, and environment · doi:10.11628/ksppe.2020.23.3.305 · Cited by 24
Kinoshita, K., Satake, S., Matsui, Y., & Arai, H. (2020) · The journal of nutrition health & aging · doi:10.1007/s12603-020-1557-3 · Cited by 22
Roth, J., Chadalawada, J., Jain, R. K., et al. (2021) · Energies · doi:10.3390/en14051481 · Cited by 22
“Despite being less popular, Bayesian statistics are not prone to some of the same misconstructions that plague frequentist statistics, such as incoherence and the interpretation of a p-value [11,12].”
BackgroundNonresponse bias in public leadership research: an empirical assessment
Vogel, D., & Jacobsen, C. B. (2021) · International Public Management Journal · doi:10.1080/10967494.2021.1906803 · Cited by 23
“Rather, null hypothesis significance testing (NHST) with p values indicates how likely such an effect (or a more extreme one) is if the null hypothesis is true (Wasserstein and Lazar 2016).”
BackgroundLi, G., Walter, S. D., & Thabane, L. (2021) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2021.03.033 · Cited by 25
Accessing Imbalance Learning Using Dynamic Selection Approach in Water Quality Anomaly Detection
Dogo, E. M., Nwulu, N. I., Twala, B., & Aigbavboa, C. O. (2021) · Symmetry · doi:10.3390/sym13050818 · Cited by 25
“Therefore, the alternative hypothesis is slightly rejected.”
Numbers (but not words) make math anxious individuals sweat: Physiological evidence
Levy, H. E., & Rubinsten, O. (2021) · Biological Psychology · doi:10.1016/j.biopsycho.2021.108187 · Cited by 26
Exposure to industrial hog operations and gastrointestinal illness in North Carolina, USA
Quist, A., Holcomb, D., Fliss, M. D., Delamater, P. L., Richardson, D. B., & Engel, L. S. (2022) · The Science of The Total Environment · doi:10.1016/j.scitotenv.2022.154823 · Cited by 23
Instagram Photo Sharing and Its Relationships With Social Connectedness, Loneliness, and Well-Being
Maclean, J., Al‐Saggaf, Y., & Hogg, R. C. (2022) · Social Media + Society · doi:10.1177/20563051221107650 · Cited by 22
“While the result is above p .05, the p value should not be the only indicator of a significant effect (Wasserstein & Lazar, 2016).”
MethodResultBommarito, P. A., Cantonwine, D. E., Stevens, D. R., Welch, B. M., Davalos, A. D., Zhao, S., McElrath, T. F., & Ferguson, K. K. (2023) · American Journal of Obstetrics and Gynecology · doi:10.1016/j.ajog.2022.10.006 · Cited by 24
Himmel, N. J., Sakurai, A., Letcher, J. M., Patel, A. A., Bhattacharjee, S., Benson, M. N., Gray, T. R., Cymbalyuk, G. S., & Cox, D. N. (2023) · eLife · doi:10.7554/elife.76863 · Cited by 21
“…that do not rely on p- values (Ioannidis, 2019; Kelter, 2020; Keysers et al., 2020; Matthews et al., 2017; Nuijten et al., 2016; van Doorn et al., 2021; Wasserstein and Lazar, 2016; Wasserstein et al., 2019; Wetzels et al., 2011), differences in mean and population proportions were analyzed using…”
Choi, H., Jovanović, J., Poquet, O., Brooks, C., Joksimovíc, S., & Williams, J. J. (2023) · The Internet and Higher Education · doi:10.1016/j.iheduc.2023.100903 · Cited by 25
Introducing the No-Significant-Effect Concentration
Fisher, R., & Fox, D. R. (2023) · Environmental Toxicology and Chemistry · doi:10.1002/etc.5610 · Cited by 23
Worschech, F., James, C. E., Jünemann, K., Sinke, C., Krüger, T. H. C., Scholz, D. S., Kliegel, M., Marié, D., & ALTENMÜLLER, E. O. (2023) · European Journal of Neuroscience · doi:10.1111/ejn.16031 · Cited by 29
“Because scientific conclusions should not be based solely on whether or not a p-value exceeds a certain threshold (Andrade, 2019; Wasserstein & Lazar, 2016), we also considered effects with 0.05 < p < 0.08 and discussed them with their COI and the exact p-value given.”
BackgroundInfluentialWatermarking Graph Neural Networks based on Backdoor Attacks
Xu, J., Koffas, S., Ersoy, O., & Picek, S. (2023) · IEEE European Symposium on Security and Privacy · doi:10.1109/eurosp57164.2023.00072 · Cited by 34
Al-Asadi, M., Sherren, M., Khalik, H. A., Leroux, T. S., Ayeni, O. R., Madden, K., & Khan, M. (2024) · The American Journal of Sports Medicine · doi:10.1177/03635465231202522 · Cited by 22
“4,40,41,59 Several measures have been subsequently developed to supplement the P value threshold, most notably, the fragility index (FI), which was first conceptualized by Feinstein 19 in 1990 and popularized for use in trials by Walsh et al 56 in 2014.”
Aasvik, O., Hagenzieker, M., Ulleberg, P., & Bjørnskau, T. (2024) · International Journal of Human-Computer Interaction · doi:10.1080/10447318.2024.2352220 · Cited by 24
“We focus on effect sizes rather than p -values when interpreting the results, as much valuable information is lost when solely looking at p values (Amrhein et al., 2019; Wasserstein & Lazar, 2016; Ziliak & McCloskey, 2008).”
Fernández-Llimós, F., Negrão, L. G., Bond, C. M., et al. (2024) · Research in Social and Administrative Pharmacy · doi:10.1016/j.sapharm.2024.06.003 · Cited by 22
Heuts, S., Ubben, J. F. H., Kawczynski, M. J., Gabrio, A., Suverein, M. M., Delnoij, T., Kaválková, P., Rob, D., Komárek, A., van der Horst, I. C. C., et al. (2024) · Critical Care · doi:10.1186/s13054-024-05008-9 · Cited by 21
“In that light, the p -value denotes the probability of observing the trial data—or more extreme—in future experiments, conditional on the assumption that the null hypothesis is true (i.e., analogous to the probability of a test result, given a disease [16, 17]).”
Background
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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