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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- 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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Teles, W. D. S., da Silva, M. C., Araújo, E. C. P., et al. (2026) · Research Society and Development · doi:10.33448/rsd-v15i4.50932
Wehden, S., Janda, K. B., & Creutzig, F. (2026) · Energy Research & Social Science · doi:10.1016/j.erss.2026.104696
Al-Anazi, S. H., Alshawakir, Y. A., Habib, S. S., Gad, H., Alotaibi, A. F., Aljasham, A. T., Althakfi, W., Mekhtiche, M. A., & Al‐Masri, A. A. (2026) · Advances in respiratory medicine · doi:10.3390/arm94030027
“Statistical interpretation of exploratory biomarker associations in small experimental cohorts should therefore be approached cautiously, particularly when evaluating biological variability and effect size estimation [29].”
Kim, M. (2026) · Research Square · doi:10.21203/rs.3.rs-9501010/v1
Incidence and risk factors related to ocular adverse effects caused by S-1 chemotherapy
Kamao, T., Yamada, M., Shiraishi, A., et al. (2026) · Japanese Journal of Ophthalmology · doi:10.1007/s10384-025-01318-9
“Univariate analysis used alpha < 0.05 to denote clinical significance [24].”
InfluentialRahaman, A. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6661758
Richters, C., Stadler, M. J., Braun, L., Fischer, M. R., & Heitzmann, N. (2026) · Journal of Computer Assisted Learning · doi:10.1002/jcal.70251
Helman, T. J., Nicholas, M., Lyon, B., Naghipour, S., Kim, C.-K., Ybanez, T., Robertson, K., Griffith, T. A., Peart, J. N., Stapelberg, N. J. C., et al. (2026) · Metabolic Brain Disease · doi:10.1007/s11011-026-01855-3
Joshi, M., Sharma, R., & Vaid, N. (2026) · Journal of Pain Research · doi:10.2147/jpr.s618258
Statistical Anatomy of a Solar Energetic Particle Event
Hristopulos, D. T., Khoo, L. Y., Cuesta, M. E., et al. (2026) · The Astrophysical Journal · doi:10.3847/1538-4357/ae5ba1
Marin, R.-C., Moleriu, R. D., Bungau, G. S., et al. (2026) · Viruses · doi:10.3390/v18050512
“In accordance with current recommendations, emphasis was placed on effect size estimates and confidence intervals rather than exclusive reliance on p -value thresholds [29].”
Sherry, A. D., Msaouel, P., Kupferman, G. S., et al. (2026) · JCO Clinical Cancer Informatics · doi:10.1200/cci-25-00303
＜統計分析で誤解をしないために＞ 第2回 統計分析の基礎知識（仮説検定の論理を知る）
金, 承. (2025) · Journal of the Society of Biomechanisms · doi:10.3951/sobim.49.2_147
Vidović, K., Krnić, J., & Benzon, B. (2026) · Medicina · doi:10.3390/medicina62050861
“As measures of evidence, R 2 , Bier index, p -values, and Bayesian posterior probabilities were used. p -values were interpreted in accordance with the recommendations of the ASA Statement on p -values [7].”
Balkin, R. S. (2026) · Measurement and Evaluation in Counseling and Development · doi:10.1080/07481756.2026.2658009
Revisiting the Intersection of Statistics and Eugenics at University College London
Burn, H. E. (2026) · Scatterplot · doi:10.1080/29932955.2026.2665847
Bryan, C. J., Tabares, J. V., Ammendola, E., & Bryan, A. O. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.121887
Certainty and Objectivity in Science and Science for Policy
Jacobs, J. R. (2026) · Science policy reports · doi:10.1007/978-3-032-09199-4_4
How Can I Evaluate the Quality and Relevance of Research on Fat Grafting for Practical Clinical Use?
Felthaus, O. (2026) · Springer eBooks · doi:10.1007/978-3-032-01711-6_7
Tartour, A. I., Chivese, T., Khan, M. N., Ahmed, M. S., Fthenou, E., & Babu, G. R. (2026) · Journal of Affective Disorders · doi:10.1016/j.jad.2026.121907 · Cited by 1
Altman, M. (2026) · Review of Behavioral Economics · doi:10.1108/rbe-11-2025-0231
Peran kesiapan digital, work-life balance, dan stres kerja dalam kepuasan kerja karyawan era digital
Palapa, G. C. I. N., & Saragih, S. R. (2026) · Journal of Management and Digital Business · doi:10.53088/jmdb.v6i1.2759
“Wasserstein & Lazar (2016) menyatakan bahwa signifikansi statistik bergantung pada perolehan p-value yang lebih kecil dari 0,05.”
Nonattainability of the Fragility Index
Heston, T. F. (2026) · Cureus · doi:10.7759/cureus.108357
“As past critiques of p-value thresholds have demonstrated, adopting rigid standards without fully understanding their functional limitations can lead to severe distortions in how scientific evidence is evaluated and reported [9].”
Kumar, V., & Ahuja, V. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6730184
Nguyen, L. T. V., Nguyen, T. Q., Duong, H. T., & Nguyen, C. T. M. (2026) · Media Asia · doi:10.1080/01296612.2026.2656600
“This approach aligns with contemporary recommendations from (Wasserstein & Lazar, 2016) to emphasize confidence intervals over dichotomous significance testing.”
Bulgac, A., Gonța, I., & Bratu, I.-R. (2026) · Frontiers in Psychology · doi:10.3389/fpsyg.2026.1811939
Dzhambov, A. M., Burov, A., Markevych, I., Kostadinov, K. R., Dimitrova, D., Helbich, M., & Nieuwenhuijsen, M. J. (2026) · International Journal of Hygiene and Environmental Health · doi:10.1016/j.ijheh.2026.114816
Lishkova, G., Schweizerhof, O., Grossmann, J., von Rohr, K., Landmesser, U., Edelmann, F. T., Eckardt, K.-U., Endres, M., Weber, J. E., Grittner, U., et al. (2026) · Cardiovascular Diabetology · doi:10.21203/rs.3.rs-9456874/v1
Statistical Misinterpretation of Nonsignificant Results in Diabetes Randomised Clinical Trials
Sharifan, A., & Fonarow, G. C. (2026) · Diabetes/Metabolism Research and Reviews · doi:10.1002/dmrr.70179
“The American Statistical Association discouraged focusing solely on p ‐values in 2016 [9] and reiterated concerns raised for decades prior.”
Burrows, K., Black‐Ingersoll, F., Sprague, N. L., Grady, S. T., Vrkljan, K. A., Ferguson, T., Just, A. C., Willis, M. D., Oblath, R., & Nori‐Sarma, A. (2026) · Environmental Epidemiology · doi:10.1097/ee9.0000000000000478
“22 All statistical analyses were performed in R (version 4.4.1).”
InfluentialHörsch, H., Schumacher, J., & Wolff, F. (2026) · Educational Psychology Review · doi:10.1007/s10648-026-10137-4 · Cited by 1
Heuermann, L., Floress, M. T., Rila, A., Wilkinson, S., & Wilzbach, A. (2026) · Journal of Positive Behavior Interventions · doi:10.1177/10983007261435204
O’Brien, K., Nguo, K., Yiu, E. M., et al. (2026) · Muscle & Nerve · doi:10.1002/mus.70275
Pavitra, S. P., Tan, K., Tan, T. K., et al. (2026) · Journal of Medical Entomology · doi:10.1093/jme/tjag051
Administered prices and product innovation: Evidence from the wheelchair industry
George, K., Minniti, M., & Rodriguez, Z. (2026) · Technovation · doi:10.1016/j.technovation.2026.103597
Taleb, F., Altaheri, A. T., Elfayoumy, R., Nourddine, S., Shurafi, S. O. A., Albakour, E. J., Hallaj, F. A., Jumaa, Z. Y., Hermis, A. H., & Mani, Z. A. (2026) · International Nursing Review · doi:10.1111/inr.70182
How much is enough? Reconsidering the role of sample size in critical care research
Steckiewicz, K. P. (2026) · Anaesthesiology Intensive Therapy · doi:10.5114/ait/221640
Constraint Generative Theory: Typed Constraint Effects and Scientific AvailabilityPreprint
Takahashi, K. (2026) · Zenodo (CERN European Organization for Nuclear Research) · doi:10.5281/zenodo.20199440
Anatomy of an article: Results section
Kholeif, A., & Badran, M. (2026) · Elsevier eBooks · doi:10.1016/b978-0-443-34264-6.00024-1
Duan, X., Jin, Y., Hong, X., et al. (2026) · Sustainability · doi:10.3390/su18105084
Rocha, J., Soares, S., Valente, A. L. G., et al. (2026) · Mathematics · doi:10.3390/math14101740
den Brok, E. J., Svensson, C. H., Olsen, M. T., Dimitropoulou, N. M., van Kuijk, S. M. J., Panagiotou, M., Rahman, L. A., Proennecke, S., Mertens, P. R., Athanasopoulos, S., et al. (2026) · Diabetes Obesity and Metabolism · doi:10.1111/dom.70879 · Cited by 1
Limsuwan, Y., Buaboon, S., Lekjinda, K., et al. (2026) · Polymer · doi:10.1016/j.polymer.2026.130246
Mayes, B. T., Morton, J. M., & Tait, L. A. (2026) · Animal Production Science · doi:10.1071/an25266
Logan, N., Neelakantan, L., Russell, M. A., & Reavley, N. J. (2026) · Research Square · doi:10.21203/rs.3.rs-9004628/v1
Cannabis Use Transitions During Early Pregnancy
Harlow, A. F., Barrington‐Trimis, J. L., Geller, R. J., Rothman, K. J., Wesselink, A. K., & Wise, L. A. (2026) · Cannabis · doi:10.26828/cannabis/2026/000353
“In line with recommendations from the American Statistical Association (Wasserstein & Lazar, 2016), we interpret estimates based on the magnitude of PRs and precision of CIs, rather than relying on null hypothesis testing and p-values.”
Wang, X., Yao, Y., Liu, X., et al. (2026) · BMC Ophthalmology · doi:10.1186/s12886-026-04933-3
“First, we quantified geometric discrepancies through relative error analysis, followed by validation against the ground truth using paired t-tests [26].”
Narcolepsy type 1: an environment-influenced disease
Vinceti, B., Fusco, C., Cordelli, D. M., & Plazzi, G. (2026) · Sleep Medicine · doi:10.1016/j.sleep.2026.109034
Kıran, Ü., Taş, O., & Uğurlu, U. (2026) · Risk Management · doi:10.1057/s41283-026-00221-y
“The American Statistical Association (ASA) statement (Wasserstein and Lazar 2016) emphasizes that p-values should not be interpreted as definitive evidence against a null hypothesis and should not be used in isolation for decision-making.”
Czortek, P., Olesik, B., & Puchałka, R. (2026) · Forest Ecology and Management · doi:10.1016/j.foreco.2026.123933
Anyaibe, S., Anderson, A. K., Domfe, C. A., Sazonov, E. E., Ghosh, T., Frost, G. S., Steiner‐Asiedu, M., Sun, M., Jia, W., Baranowski, T., et al. (2026) · Chronobiology International · doi:10.1080/07420528.2026.2674741
“This limitation underscores the arbitrary nature of p -value thresholds, a critique well-documented by statisticians who emphasize that p -values are not absolute measures of effect size or importance but rather probabilistic tools for inference (Wasserstein and Lazar 2016; Ionides et al. 2017).”
Applying Statistics in Your EEG Research
Viswanathan, S. (2026) · Springer eBooks · doi:10.1007/978-3-032-20450-9_9
The Hidden Landscape of Missed Effects in Human Functional NeuroimagingPreprint
Noble, S., Shearer, H., Rosenblatt, M., Ye, J., Jiang, R., Tejavibulya, L., Foster, M. L., Liang, Q., Dadashkarimi, J., Westwater, M. L., et al. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.21.726948
Samee, N., Belz, L., Narboux‐Nême, N., et al. (2026) · Cells · doi:10.3390/cells15100948
Sampson, L., Bonauto, D. K., & Marcum, J. L. (2026) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph23060699
“We assessed differences using both the point estimates and width of the 95% confidence intervals [36,37].”
Debiasing for trustworthy AI-based prediction of preterm birth
Mpousiou, E., Rigas, E. S., Tsakiridis, I. G., et al. (2026) · Health Informatics Journal · doi:10.1177/14604582261454199
Chen, L., & Ma, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6835335
Mañana-Rodríguez, J., Granadino-Goenechea, B., & Bautista-Puig, N. (2026) · Scientometrics · doi:10.1007/s11192-026-05668-0 · Cited by 1
Carr, J. M. J. R., Koep, J. L., Duffy, J. S., Brewster, L. M., Bird, J. D., Monteleone, J. A., Monaghan, T. D. R., Islam, H., Steele, A. R., Howe, C. A., et al. (2026) · Journal of Neurochemistry · doi:10.1111/jnc.70488
“Reducing scientific inference to recourse to “bright-line” rules (such as “ p 0.05”) for justifying scientific claims can lead to erroneous conclusions (Wasserstein and Lazar 2016).”
InfluentialVeltri, G. (2025) · Elsevier eBooks · doi:10.1016/b978-0-443-40622-5.00046-3
The Hypothesis Race Model for evaluation of research findingsPreprint
Kelly, R. E. (2026) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.64898/2026.05.28.728385
“Null hypothesis significance testing (NHST) has provided the backbone of our evidence-24 based medicine, but a statistically significant result alone does not answer the important question of how confident we can be that a given hypothesis is true [1–4].”
Robust Sequential Statistical Testing for Physiological Time-Series Using the Safe Test FrameworkPreprint
Sun, J., Shen, A., Bi, Y., Hu, N., & Sun, Y. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6849337
Reevaluating p-value and its impact on conventional results display and interpretation
Avdović, A., & Vidović, Z. (2025) · Filomat · doi:10.2298/fil2534327a
Peng, L., Wei, J., Yang, H., Qin, J., & Li, Q. (2026) · Osteoarthritis and Cartilage · doi:10.1016/j.joca.2026.05.013
Animal-assisted treatment in spinal cord injury rehabilitation: a scoping review
Williamson, S. D., Aaby, A., Ejersbo, A. O., Glintborg, C., & Ravn, S. L. (2026) · Spinal Cord · doi:10.1038/s41393-026-01223-z
Günver, M., Sert, M. F., Yurtseven, E., Sezgin, M., Sayılı, U., Canbaz, M., Çulpan, H. C., Can, G., Erginöz, E., Yusuf, M., et al. (2026) · Scandinavian Journal of Clinical and Laboratory Investigation · doi:10.1080/00365513.2026.2681040
“The present findings also reinforce the importance of interpreting p -values together with effect sizes rather than in isolation [1,10,14].”
Early-life nutritional status as a determinant of bone density and hip joint morphology in children
Liu, H., Zhang, W., Chen, G., et al. (2026) · Frontiers in Nutrition · doi:10.3389/fnut.2026.1797867
“Moreover, more attention was paid to effect estimates and their corresponding confidence intervals rather than the single use of p -values, which is also consistent with current recommendations for statistical reporting (12, 13).”
Vuong, Q., Nguyen, P.-T., & Nguyen, M.-H. (2026) · Research Square · doi:10.21203/rs.3.rs-9456872/v1
Decoding Decisions: Personality-Interest Motivational Sequences as Predictors of Career Paths
Townsend, G. C., & Webb, P. (2026) · F1000Research · doi:10.12688/f1000research.176767.2
Chan, S., Zhou, D., Zhang, D., Xia, Y., & Tang, W. (2026) · Vaccines · doi:10.3390/vaccines14060489
“…were initially performed to examine associations between individual trial design characteristics and progression outcomes across major feature domains, including sample size, vaccine purpose, disease type, age group, study center structure, funding source, randomization, control, and blinding [22].”
Comprehensive evaluation of helium bubbling for extending storage time in cryogenic tanksPreprint
Qu, Z., Li, Y., Song, G., et al. (2026) · SSRN Electronic Journal · doi:10.2139/ssrn.6866479
Analytic posterior distribution and bayes factor for pearson partial correlations
Kucharský, Š., Wagenmakers, E., van den Bergh, D., & Ly, A. (2026) · Test · doi:10.1007/s11749-026-01006-x · Cited by 2
Predictive Power Analysis of Multiple Test Procedures Under Arbitrary Dependence
Karabatsos, G. (2026) · Stats · doi:10.3390/stats9030056
“Meanwhile, p -values provide a common “bottom line” language for the communication of statistical results that are still very often communicated by scientific journals, despite concerns [7].”
Wu, C.-H., Lye, L.-F., & Su, T. (2026) · Environmental Pollution · doi:10.1016/j.envpol.2026.128500
Méndez, E. M. I., & Pérez, G. M. L. (2021) · [in]genio revista de investigación y labor creativa · doi:10.54114/ingeniosv7i2.15429
“However, Wasserstein & Lazar (2016) pointed out that this statement was made with the purpose of satisfying a definition in which both Bayesian statisticians and frequentist statisticians could agree upon, not to tackle the underlying problem of misinterpretation brought by Ioannidis (2018) or…”
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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