The DOI matches its Crossref record; 110 of the 110 references it cites were found in a registry or an index; 11 of the 39 sources its claims cite were checked for support.
Power failure: why small sample size undermines the reliability of neuroscience
Open accessAbstract
A study with low statistical power has a reduced chance of detecting a true effect, but it is less well appreciated that low power also reduces the likelihood that a statistically significant result reflects a true effect. Here, we show that the average statistical power of studies in the neurosciences is very low. The consequences of this include overestimates of effect size and low reproducibility of results. There are also ethical dimensions to this problem, as unreliable research is inefficient and wasteful. Improving reproducibility in neuroscience is a key priority and requires attention to well-established but often ignored methodological principles.
Overview
AI snapshot
Written from the paper’s full text.
TL;DR
This review argues that low statistical power is widespread in neuroscience and that it makes published findings less likely to be true, more likely to overstate effects, and harder to replicate. Across meta-analyses from several subfields, median power was often below 31%, with some estimates as low as 8%.
Key findings
- In a sample of 48 articles, 49 meta-analyses and 730 primary studies, the median statistical power in neuroscience was 21%; excluding seven unusually high-powered neurological meta-analyses, it fell to 18%.
- The observed number of nominally significant studies was 349, compared with 254 expected under the meta-analytic summary effects, indicating excess significance (p < 0.0001).
- For brain volume studies, median power was 8% across 461 individual studies from 41 meta-analyses in eight articles; for animal water-maze and radial-maze studies, median power was 18% and 31%, respectively, with average sample sizes of 22 and 24 animals.
- With pre-study odds of 0.25 and α = 0.05, 20% power yielded a positive predictive value of 0.50, whereas 80% power yielded 0.80; simulations suggested initial effect estimates from studies powered between about 8% and 31% are inflated by 25% to 50%.
Methods
- The authors searched Web of Science on 2 February 2012 for 2011 articles using the terms “neuroscience” and “meta-analysis,” screened records independently, and included 48 articles containing 49 meta-analyses and 730 primary studies.
- They extracted summary effect sizes and study-level sample sizes or event counts, then calculated each primary study’s achieved power to detect its corresponding meta-analytic summary effect at α = 5% using G*Power, reporting mean and median power and testing for excess significance.
- Because the main sample underrepresented neuroimaging and animal-model research, they added representative meta-analyses from those areas outside the 2011 sampling frame.
Limitations
- The paper states that true effect sizes are unknown, so meta-analytic summary effects were used as proxies; because those summaries may themselves be inflated by publication and other biases, the power estimates are likely optimistic.
- The authors note that the main analysis underrepresented neuroimaging and animal-model subfields, and that the animal-model findings rest on only two meta-analyses, so extrapolation should be cautious.
- Evident from the text: the search was limited to 2011 Web of Science records and to meta-analyses with extractable data, and the excess-significance test depends on the assumption that the summary effects approximate the true effects.
Model-written summary. Check it against the paper before relying on it.
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Claims the paper makes
11 of 39 cited sources checked · 6 supported · 0 flagged · accuracy not yet measured
Every source the paper cites for this claim was checked and supports it.
In the paper
“As a consequence, researchers have strong incentives to engage in research practices that make their findings publishable quickly, even if those practices reduce the likelihood that the findings reflect a true (that is, non-null) effect 4 .”
§ Power failure: why small sample size undermines the reliability of neuroscience > Abstract
What the sources say
- Supportedcites [4] Nosek, B. A., Spies, J. R. & Motyl, M. Scientific utopia: II. Restructuring incentives and practices to promote truth over publishability. Perspect. Psychol. Sci. 7, 615–631 (2012). source
The Abstract says publication incentives encourage research decisions that produce positive results and inflate the rate of false effects. The section “Strategies That Will Accelerate the Accumulation of Knowledge” contrasts the immediate payoff of publishing with the longer-term goal of accuracy, supporting the claim’s emphasis on publishing quickly. (LLM, conf=0.92)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“When studies in a given field are designed with a power of 20%, it means that if there are 100 genuine non-null effects to be discovered in that field, these studies are expected to discover only 20 of them 11 .”
§ Power failure: why small sample size undermines the reliability of neuroscience > Abstract
What the sources say
- Supportedcites [11] Sterne, J. A. & Davey Smith, G. Sifting the evidence — what's wrong with significance tests? BMJ 322, 226–231 (2001). source
Table 3 defines 20% power as rejecting the null hypothesis 20% of the time when it is false. Thus, among 100 genuine non-null effects, the expected number detected is 20. The supplied copy does not specify a page number. (LLM, conf=0.93)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“Second, the lower the power of a study, the lower the probability that an observed effect that passes the required threshold of claiming its discovery (that is, reaching nominal statistical significance, such as p < 0.05) actually reflects a true effect 1,12 .”
§ Power failure: why small sample size undermines the reliability of neuroscience > Abstract
What the sources say
- Supportedcites [1] Ioannidis, J. P. Why most published research findings are false. PLoS Med. 2, e124 (2005). This study demonstrates that many (and possibly most) of the conclusions drawn from biomedical research are probably false. The reasons for this include using flexible study designs and flexible statistical analyses and running small studies with low statistical power. source
Corollary 1 states that, other factors being equal, the probability that a research finding is true decreases as study power decreases. Table 4 identifies this probability as the positive predictive value of a finding under a nominal significance threshold of α = 0.05. (LLM, conf=0.94)
- Not checked — no full text of the sourcecites [12] Ioannidis, J. P. A., Tarone, R. & McLaughlin, J. K. The false-positive to false-negative ratio in epidemiologic studies. Epidemiology 22, 450–456 (2011). source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Previous analyses of studies using animal models have shown that small studies consistently give more favourable (that is, 'positive') results than larger studies 78”
§ Power failure: why small sample size undermines the reliability of neuroscience > Animal model studies.
What the sources say
- Partly supportedcites [78] Sena, E. S., van der Worp, H. B., Bath, P. M., Howells, D. W. & Macleod, M. R. Publication bias in reports of animal stroke studies leads to major overstatement of efficacy. PLoS Biol. 8, e1000344 (2010). sourceExperimental — accuracy not yet measured
The Results (Prevalence of Publication Bias; page not provided) report that all 16 datasets had an excess of imprecise studies with large effect sizes, supporting the favourable-results pattern in animal stroke studies. But the Discussion (page not provided) cautions that precision in these studies relates more to biological variability and measurement error than to study size. The paper therefore does not establish the claimed comparison of small studies with larger studies. (LLM, conf=0.86)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Partly supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“study quality is inversely related to effect size [79][80][81][82]”
§ Power failure: why small sample size undermines the reliability of neuroscience > Animal model studies.
What the sources say
- Not checked — no full text of the sourcecites [79] Ioannidis, J. P. Extrapolating from animals to humans. Sci. Transl. Med. 4, 151ps15 (2012). source
- Not checked — no full text of the sourcecites [80] Jonasson, Z. Meta-analysis of sex differences in rodent models of learning and memory: a review of behavioral and biological data. Neurosci. Biobehav. Rev. 28, 811–825 (2005). source
- Partly supportedcites [81] Macleod, M. R. et al. Evidence for the efficacy of NXY-059 in experimental focal cerebral ischaemia is confounded by study quality. Stroke 39, 2824–2829 (2008). sourceExperimental — accuracy not yet measured
The Results report that estimated efficacy was highest in lower-quality studies of NXY-059 in animal models of stroke. This supports the inverse relationship in the studies analyzed, but does not establish it across animal model studies generally. (LLM, conf=0.84)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesThe cited sources support only part of this claim — read each source’s verdict below.
In the paper
“A requirement for sample size and power calculation is included in the Animal Research: Reporting In Vivo Experiments (ARRIVE) guidelines 84”
§ Power failure: why small sample size undermines the reliability of neuroscience > Ethical implications.
What the sources say
- Partly supportedcites [84] Kilkenny, C., Browne, W. J., Cuthill, I. C., Emerson, M. & Altman, D. G. Improving bioscience research reporting: the ARRIVE guidelines for reporting animal research. PLoS Biol. 8, e1000412 (2010). sourceExperimental — accuracy not yet measured
Table 2, item 10 (Sample size), asks authors to report animal numbers, explain how they were decided, and provide details of any sample-size calculation used. It does not require a calculation or mention a power calculation; the accompanying text also says the guidelines are not intended to be mandatory. (LLM, conf=0.89)
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passes- Partly supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our results indicate that the median statistical power in neuroscience is 21%.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Results.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes- Rests on an assumption: The 49 meta-analyses included are representative of neuroscience research.
In the paper
“Our results indicated that the median statistical power of these studies was 8% across 461 individual studies contributing to 41 separate meta-analyses, which were drawn from eight articles that were published between 2006 and 2009.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Neuroimaging studies.
What the sources say
- Not checked — no full text of the sourcecites [73] Ioannidis, J. P. Excess significance bias in the literature on brain volume abnormalities. Arch. Gen. Psychiatry 68, 773–780 (2011). source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesNone of the sources cited for this claim have been checked yet.
In the paper
“However, seven meta-analyses comprised studies with high (>90%) average power 24,26,31,57,63,68,71 .”
§ Power failure: why small sample size undermines the reliability of neuroscience > Results.
What the sources say
- Not checked — no full text of the sourcecites [24] Babbage, D. R. et al. Meta-analysis of facial affect recognition difficulties after traumatic brain injury. Neuropsychology 25, 277–285 (2011). source
- Not checked — no full text of the sourcecites [26] Bjorkhem-Bergman, L., Asplund, A. B. & Lindh, J. D. Metformin for weight reduction in non-diabetic patients on antipsychotic drugs: a systematic review and meta-analysis. J. Psychopharmacol. 25, 299–305 (2011). source
- Not checked — no full text of the sourcecites [31] Chen, C. et al. Allergy and risk of glioma: a meta-analysis. Eur. J. Neurol. 18, 387–395 (2011). source
- Not checked — no full text of the sourcecites [57] Tian, Y., Kang, L. G., Wang, H. Y. & Liu, Z. Y. Meta-analysis of transcranial magnetic stimulation to treat post-stroke dysfunction. Neural Regen. Res. 6, 1736–1741 (2011).
- Not checked — no full text of the sourcecites [63] Witteman, J., van Ijzendoorn, M. H., van de Velde, D., van Heuven, V. J. & Schiller, N. O. The nature of hemispheric specialization for linguistic and emotional prosodic perception: a meta-analysis of the lesion literature. Neuropsychologia 49, 3722–3738 (2011). source
- Not matchedcites [68] Yuan, H. et al. Meta-analysis of tau genetic polymorphism and sporadic progressive supranuclear palsy susceptibility. Neural Regen. Res. 6, 353–359 (2011).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
- Not checked — no full text of the sourcecites [71] Zhu, Y., He, Z. Y. & Liu, H. N. Meta-analysis of the relationship between homocysteine, vitamin B(12), folate, and multiple sclerosis. J. Clin. Neurosci. 18, 933–938 (2011). source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesA result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“If we exclude these 'outlying' meta-analyses, the median statistical power falls to 18%.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Results.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passes
- It may state two separate claims as one. 21
- Probably not a substantial claim. 19
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“Such practices include using flexible study designs and flexible statistical analyses and running small studies with low statistical power 1,5 .”
§ Power failure: why small sample size undermines the reliability of neuroscience > Abstract
What the sources say
- Supportedcites [1] Ioannidis, J. P. Why most published research findings are false. PLoS Med. 2, e124 (2005). This study demonstrates that many (and possibly most) of the conclusions drawn from biomedical research are probably false. The reasons for this include using flexible study designs and flexible statistical analyses and running small studies with low statistical power. source
Corollary 4 says that flexibility in study designs and analytical methods makes findings less likely to be true. Corollary 1 says that smaller studies have lower statistical power and that the probability a finding is true falls as power decreases. Together, these support the practices attributed to the paper. (LLM, conf=0.94)
- Not checked — no full text of the sourcecites [5] Simmons, J. P., Nelson, L. D. & Simonsohn, U. False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol. Sci. 22, 1359–1366 (2011). This article empirically illustrates that flexible study designs and data analysis dramatically increase the possibility of obtaining a nominally significant result. However, conclusions drawn from these results are almost certainly false. source
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Supported
Every source the paper cites for this claim was checked and supports it.
In the paper
“It has been claimed and demonstrated that many (and possibly most) of the conclusions drawn from biomedical research are probably false 1 .”
§ Power failure: why small sample size undermines the reliability of neuroscience > Abstract
What the sources say
- Supportedcites [1] Ioannidis, J. P. Why most published research findings are false. PLoS Med. 2, e124 (2005). This study demonstrates that many (and possibly most) of the conclusions drawn from biomedical research are probably false. The reasons for this include using flexible study designs and flexible statistical analyses and running small studies with low statistical power. source
The Abstract says there is increasing concern that most published research findings are false and that simulations show a research claim is more likely to be false than true for most study designs and settings. The paper’s discussion of biomedical research makes the citing sentence a fair paraphrase of its argument. (LLM, conf=0.92)
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Supported
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Second, publication bias, selective data analysis and selective reporting of outcomes are more likely to affect low-powered studies.”
- Also stated in: § Low power in the presence of other biases · hedged
“Publication bias and selective reporting of outcomes and analyses are also more likely to affect smaller, underpowered studies 17 .”
§ Power failure: why small sample size undermines the reliability of neuroscience > Low power in the presence of other biases
What the sources say
- Partly supportedcites [17] Dwan, K. et al. Systematic review of the empirical evidence of study publication bias and outcome reporting bias. PLoS ONE 3, e3081 (2008). sourceExperimental — accuracy not yet measured
The Discussion (page unavailable) reports that positive or statistically significant studies are more likely to be published and significant outcomes more likely to be fully reported. The paper does not establish that publication bias or selective reporting is more likely in smaller, low-powered studies; the Results mention power calculations but do not make that comparison. Its Introduction describes selective reporting of analyses but does not assess whether it varies by study power. (LLM, conf=0.83)
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
- Also stated in: § Low power in the presence of other biases · hedged
The cited sources support only part of this claim — read each source’s verdict below.
In the paper
“Although previously almost all of the proposed candidate gene associations from small studies were false 99 (with some exceptions 100 ), collaborative consortia have substantially improved power, and the replicated results can be considered highly reliable.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Incentivizing replication.
What the sources say
- Not checked — no full text of the sourcecites [99] Ioannidis, J. P., Tarone, R. & McLaughlin, J. K. The false-positive to false-negative ratio in epidemiologic studies. Epidemiology 22, 450–456 (2011). source
- Partly supportedcites [100] Siontis, K. C., Patsopoulos, N. A. & Ioannidis, J. P. Replication of past candidate loci for common diseases and phenotypes in 100 genome-wide association studies. Eur. J. Hum. Genet. 18, 832–837 (2010). sourceExperimental — accuracy not yet measured
Split into 2 atoms — 0 confirmed, 2 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
The sources with a verdict support this claim; the rest weren’t checked yet, have no full text we could read, or their verdicts aren’t rated yet.
In the paper
“However, these policies are uncommonly adhered to 95 , and thus the ability for independent experts to repeat published analysis remains low 96 .”
§ Power failure: why small sample size undermines the reliability of neuroscience > Improving availability of materials and data.
What the sources say
- Supportedcites [95] Alsheikh-Ali, A. A., Qureshi, W., Al-Mallah, M. H. & Ioannidis, J. P. Public availability of published research data in high-impact journals. PLoS ONE 6, e24357 (2011). source
The Discussion states that more than half of publications subject to journal data-availability instructions did not adhere to them. This supports the policy-adherence clause attributed to this source; the separate claim about repeating analyses carries citation 96. (LLM, conf=0.93)
- Not checked — no full text of the sourcecites [96] Ioannidis, J. P. et al. Repeatability of published microarray gene expression analyses. Nature Genet. 41, 149–155 (2009). source
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes? Load-bearing: unclear✓ Based: passesIt may state two separate claims as one.
- Supported
A result the paper reports from its own data — it cites no other source for it, so there is none to check. The quote shows it is in the paper.
In the paper
“Our results indicate that the median statistical power for the water maze studies and the radial maze studies to detect these medium to large effects was 18% and 31%, respectively (TABLE 2).”
§ Power failure: why small sample size undermines the reliability of neuroscience > Animal model studies.
Load-bearingStrength:hedged· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — 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 average sample size in these studies was 22 animals for the water maze and 24 for the radial maze experiments.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Animal model studies.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
“In order to achieve 80% power to detect, in a single study, the most probable true effects as indicated by the meta-analysis, a sample size of 134 animals would be required for the water maze experiment (assuming an effect size of d = 0.49) and 68 animals for the radial maze experiment (assuming an effect size of d = 0.69); to achieve 95% power, these sample sizes would need to increase to 220 and”
§ Power failure: why small sample size undermines the reliability of neuroscience > Ethical implications.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
A result the paper reports from its own data — 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 total numbers of animals actually used in the studies contributing to the meta-analyses were even larger: 420 for the water maze experiments and 514 for the radial maze experiments.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Ethical implications.
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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 meta-analyses (n = 7) that comprised studies with high average power in excess of 90% had their broadly neurological subject matter in common.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Work collaboratively to increase power and replicate findings
Tests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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
“First, low power, by definition, means that the chance of discovering effects that are genuinely true is low.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Abstract
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
A claim is checked only against the cited paper’s full text, never its abstract, and none is available for its sources.
In the paper
“Given that these publishing biases are pervasive across scientific practice, it is possible that false positives heavily contaminate the neuroscience literature as well, and this problem may affect at least as much, if not even more so, the most prominent journals 9,10 .”
§ Power failure: why small sample size undermines the reliability of neuroscience > Abstract
What the sources say
- Not checked — no full text of the sourcecites [9] Fang, F. C. & Casadevall, A. Retracted science and the retraction index. Infect. Immun. 79, 3855–3859 (2011). source
- Not checked — no full text of the sourcecites [10] Munafo, M. R., Stothart, G. & Flint, J. Bias in genetic association studies and impact factor. Mol. Psychiatry 14, 119–120 (2009). source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
“Pre-registration -and, ultimately, full reporting of analysis plans -clarifies the distinction between confirmatory and exploratory analysis, encourages well-powered studies (at least in the case of confirmatory analyses) and reduces the file-drawer effect.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Registration of confirmatory analysis plan.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“The principles of the 'three Rs' in animal research (reduce, refine and replace) 83 require appropriate experimental design and statistics -both too many and too few animals present an issue as they reduce the value of research outputs.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Ethical implications.
What the sources say
- Not matchedcites [83] Russell, W. M. S. & Burch, R. L. The Principles of Humane Experimental Technique (Methuen, 1958).
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it. It may state two separate claims as one.
- Not matched
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“All of the articles that were identified via this electronic search were screened independently for suitability by two authors (K.S.B. and M.R.M.).”
§ Power failure: why small sample size undermines the reliability of neuroscience > Methods.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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
“Low power therefore has an ethical dimensionunreliable research is inefficient and wasteful.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Ethical implications.
Tests✓ Asserted: passes✗ Checkable: fails✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim. It may be too vague to check against evidence. It may state two separate claims as one.
- Rests on an assumption: Low power is a problem in practice because of the normative publishing standards for producing novel, significant, clean results and the ubiquity of null hypothesis significance testing as the means of evaluating the truth of research findings.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“The results described in this section are based on only two meta-analyses, and we should be appropriately cautious in extrapolating from this limited evidence.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Animal model studies.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably 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
“Nevertheless, it is notable that the results are so consistent with those observed in other fields, such as the neuroimaging and neuroscience studies that we have described above.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Animal model studies.
Strength:plain· not yet calibratedCausal:correlational· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
In the paper
“False positives occur more frequently and go unnoticed when degrees of freedom in data analysis and reporting are undisclosed 5 .”
§ Power failure: why small sample size undermines the reliability of neuroscience > Conclusions and future directions
What the sources say
- Not checked — no full text of the sourcecites [5] Simmons, J. P., Nelson, L. D. & Simonsohn, U. False-positive psychology: undisclosed flexibility in data collection and analysis allows presenting anything as significant. Psychol. Sci. 22, 1359–1366 (2011). This article empirically illustrates that flexible study designs and data analysis dramatically increase the possibility of obtaining a nominally significant result. However, conclusions drawn from these results are almost certainly false. source
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
“Making research materials available will improve the quality of studies aimed at replicating and extending research findings.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Improving availability of materials and data.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably not a substantial claim. The paper’s conclusions may not depend on it. The paper may give no reason why it holds.
What the paper did — it cites no source for this, so there is none to check. The quote shows it is in the paper.
In the paper
“Data extraction was performed independently by K.S.B. and M.R.M. or C.M. and verified collaboratively.”
- Also stated in: § Figure 2 | Flow diagram of articles selected for inclusion. · plain
“Data extraction was independently performed by K.S.B. and either M.R.M. or C.M. and verified collaboratively.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Figure 2 | Flow diagram of articles selected for inclusion.
Strength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
- Also stated in: § Figure 2 | Flow diagram of articles selected for inclusion. · plain
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
“Meta-analysis provides the best estimate of the true effect size, albeit with limitations, including the limitation that the individual studies that contribute to a meta-analysis are themselves subject to the problems described above.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Empirical evidence from neuroscience
Load-bearingStrength:plain· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
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In the paper
“As we have already discussed, several other biases are also likely to reduce the probability that a research finding reflects a true effect.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Implications for the likelihood that a research finding reflects a true effect.
Strength:hedged· not yet calibratedCausal:conditional· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably 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.
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
“Inflated effect estimates make it difficult to determine an adequate sample size for replication studies, increasing the probability of type II errors.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Work collaboratively to increase power and replicate findings
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt 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
“Null findings locked in file drawers bias the literature, whereas exploratory analyses are only useful and valid if you acknowledge the caveats and limitations.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Disclose methods and findings transparently
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✗ Checkable: fails✗ Load-bearing: fails✗ Based: failsProbably 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.
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
“Assuming, as one might in a power calculation, that the initially observed effect we are trying to replicate reflects the true effect, the potential distribution of these replication effect estimates would be similar to the distribution of the original study (dashed green curve).”
§ Power failure: why small sample size undermines the reliability of neuroscience > Work collaboratively to increase power and replicate findings
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- The authors state this as an assumption: The initially observed effect that a replication study attempts to replicate reflects the true effect.
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
“Some fields are confronting the problem of the poor reliability of research findings that arises from low-powered studies.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Concluding remarks.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“We can increase the power of the replication study (coloured area under the orange curve) by increasing the sample size so as to reduce the sem.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Work collaboratively to increase power and replicate findings
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In the paper
“For example, in genetic epidemiology sample sizes increased dramatically with the widespread understanding that the effects being sought are likely to be extremely small.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Concluding remarks.
Strength:plain· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✓ Based: passesProbably not a substantial claim. The paper’s conclusions may not depend on it.
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In the paper
“If a study finds evidence for an effect at p = 0.05, then the difference between the mean of the null distribution (indicated by the solid blue curve) and the mean of the observed distribution (dashed blue curve) is 1.96 × sem.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Work collaboratively to increase power and replicate findings
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In the paper
“Making raw data available will improve data aggregation methods and confidence in reported results.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Improving availability of materials and data.
Strength:hedged· not yet calibratedCausal:direct· not yet calibratedTests✓ Asserted: passes✓ Checkable: passes✗ Load-bearing: fails✗ Based: failsProbably 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
“Combining data increases the total sample size (and therefore power) while minimizing the labour and resource impact on any one contributor.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Work collaboratively to increase power and replicate findings
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✗ Based: failsProbably 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
“By contrast, large, high-powered studies can readily detect both small and large effects and so are less biased, as both over-and underestimations of the true effect size will pass the threshold for 'discovery'.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Work collaboratively to increase power and replicate findings
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesIt may state two separate claims as one.
In the paper
“The winner's curse refers to the phenomenon that studies that find evidence of an effect often provide inflated estimates of the size of that effect.”
§ Power failure: why small sample size undermines the reliability of neuroscience > Work collaboratively to increase power and replicate findings
What the sources say
- Not matchedcites REF. 103
Couldn’t be matched to a paper in Crossref, so it can’t be checked.
Tests✓ Asserted: passes✓ Checkable: passes✓ Load-bearing: passes✓ Based: passesProbably not a substantial claim.
- Not matched
The authors state this as an assumption.
The summary effect size reported in each study is close to the true effect size.
“Importantly, these calculations assume that the summary effect size reported in each study is close to the true effect size, but it is likely that they are inflated owing to publication and other biases described above.”
The authors state this as an assumption.
The initially observed effect we are trying to replicate reflects the true effect.
“Assuming, as one might in a power calculation, that the initially observed effect we are trying to replicate reflects the true effect, the potential distribution of these replication effect estimates would be similar to the distribution of the original study (dashed green curve).”
The paper relies on this without support.
The 49 meta-analyses included are representative of neuroscience research.
“Our results indicate that the median statistical power in neuroscience is 21%.”
The paper relies on this without support.
Most structural and volumetric MRI studies are very small and have minimal power to detect differences between compared groups.
“Most structural and volumetric MRI studies are very small and have minimal power to detect differences between compared groups (for example, healthy people versus those with mental health diseases).”
The paper relies on this without support.
The effects now sought in neuroscience experiments are often smaller and more subtle than before.
“A consequence of the remarkable growth in neuroscience over the past 50 years has been that the effects we now seek in our experiments are often smaller and more subtle than before as opposed to when mostly easily discernible 'low-hanging fruit' were targeted.”
The paper relies on this without support.
Computational analysis of very large datasets is now relatively straightforward, so that an enormous number of tests can be run in a short time on the same dataset.
“At the same time, computational analysis of very large datasets is now relatively straightforward, so that an enormous number of tests can be run in a short time on the same dataset.”
The paper relies on this without support.
These dramatic advances in the flexibility of research design and analysis have occurred without accompanying changes to other aspects of research design, particularly power.
“These dramatic advances in the flexibility of research design and analysis have occurred without accompanying changes to other aspects of research design, particularly power.”
The paper relies on this without support.
Low power is a problem in practice because of the normative publishing standards for producing novel, significant, clean results and the ubiquity of null hypothesis significance testing as the means of evaluating the truth of research findings.
“Low power is a problem in practice because of the normative publishing standards for producing novel, significant, clean results and the ubiquity of null hypothesis significance testing as the means of evaluating the truth of research findings.”
The authors state this as an assumption.
The initially observed effect that a replication study attempts to replicate reflects the true effect.
“Assuming, as one might in a power calculation, that the initially observed effect we are trying to replicate reflects the true effect”
The paper relies on this without support.
In an exploratory research field such as much of neuroscience, the pre-study odds are often low.
“Notably, in an exploratory research field such as much of neuroscience, the pre-study odds are often low.”
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 cited source doesn’t support the claim as written.
In the citing paper
“Due to differences in age and sample size between protocols and phases, effect sizes could not be directly compared (Button et al., 2013), but the directions of associations with age were largely consistent for ADNI2 and ADNI3 phases separately, and each ADNI3 protocol (Figures 1, 2).”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Button et al., 2013
Checked by a reasoning model — may be wrong.
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
“but not very different from studies in neuroscience in general (average power = 21%) (14).”
From Statistical and Methodological Considerations for the Interpretation of Intranasal Oxytocin Studies (2016) · cites this paper as [14]
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
“First, the sample size is small, which precludes the detection of small or medium-sized effects.43”
From Effects of Insufficient Sleep on Pituitary–Adrenocortical Response to CRH Stimulation in Healthy Men (2017) · cites this paper as [43]
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
“The lower response rate at week 26 may also result in inflated effects sizes and lower reproducibility of results [63].”
From Benchmark findings from a veteran electronic patient-reported outcomes evaluation from a chronic pain management telehealth program (2024) · cites this paper as [63]
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
“This may benefit studies in particular that focus on neuroimaging, which are costly and tend to have small sample sizes (Button et al. 2013), those that employ electrophysiological techniques that might be extremely time-consuming, or those that include genetics.”
From New directions in hypnosis research: strategies for advancing the cognitive and clinical neuroscience of hypnosis (2017) · cites this paper as Button et al. 2013
Experimental — accuracy not yet measured
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
“The current study is limited in that the sample sizes and sample demographics available for each protocol vary, complicating direct comparison of the protocols (Button et al., 2013).”
From Diffusion MRI Indices and Their Relation to Cognitive Impairment in Brain Aging: The Updated Multi-protocol Approach in ADNI3 (2019) · cites this paper as Button et al., 2013
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“This might indicate that results from previous studies do not always reflect a true effect (Button et al. 2013)”
From Terror Management Theory in the Consumer Domain: A Systematic Review and Meta‐Analysis on Mortality Salience Driven Consumer Responses (2025) · cites this paper as Button et al. 2013
The cited source says what the claim attributes to it.
In the citing paper
“Another limitation was the small sample size, due to the duration of the project (just over a year) and the availability of families to take their children for evaluation within this period. This could have reduced the statistical power of the study, potentially lowering the probability of detecting a true effect if one exists and reducing the likelihood that a statistically significant result”
From Enhancing Ecological Validity: Virtual Reality Assessment of Executive Functioning in Children and Adolescents with ADHD (2024) · cites this paper as [46]
The cited source says what the claim attributes to it.
In the citing paper
“Selective reporting, questionable research practices, and low-powered research all contribute to the unreliability of findings in a range of disciplines”
From Investigating the replicability of preclinical cancer biology (2021) · cites this paper as Button et al., 2013
The cited source says what the claim attributes to it.
In the citing paper
“Given the tendency for small sample sizes to produce inflated effect size estimates (Button et al., 2013), future studies should include larger samples in order to produce more precise effect size estimates.”
From Working Memory Training Does Not Improve Performance on Measures of Intelligence or Other Measures of “Far Transfer”: Evidence From a Meta-Analytic Review (2016) · cites this paper as Button et al., 2013
The cited source says what the claim attributes to it.
In the citing paper
“Fourth, due to the typical practices in neuroscience research of obtaining and relying on small and underpowered samples (Button et al., 2013).”
From Is the Putative Mirror Neuron System Associated with Empathy? A Systematic Review and Meta-Analysis (2021) · cites this paper as Button et al., 2013
The cited source says what the claim attributes to it.
In the citing paper
“Sample sizes of included studies were, on average, relatively small, which is consistent with the recent findings of Button et al. (2013) that suggest such sample sizes are typical in neuroscience research.”
From Is the Putative Mirror Neuron System Associated with Empathy? A Systematic Review and Meta-Analysis (2021) · cites this paper as Button et al. (2013)
The cited source says what the claim attributes to it.
In the citing paper
“Potentially problematic practices include selective reporting, selective analysis, and insufficient specification of the conditions necessary or sufficient to obtain the results ( 1223 ).”
From Estimating the reproducibility of psychological science (2015) · cites this paper as [14]
The cited source says what the claim attributes to it.
In the citing paper
“An important qualification about this result is the possibility that the original studies have inflated effect sizes due to publication, selection, reporting, or other biases ( 9, 1223 ).”
From Estimating the reproducibility of psychological science (2015) · cites this paper as [14]
The cited source says what the claim attributes to it.
In the citing paper
“Concerns about reproducibility are widespread across disciplines ( 921 ).”
From Estimating the reproducibility of psychological science (2015) · cites this paper as [14]
The cited source says what the claim attributes to it.
In the citing paper
“There is good evidence that low power of research designs combined with publication bias favoring positive results over negative results produces a literature with upwardly biased effect sizes ( 14, 16, 30, 31 ).”
From Estimating the reproducibility of psychological science (2015) · cites this paper as [14]
The cited source says what the claim attributes to it.
In the citing paper
“and has contributed, in part, to the replication crisis in other fields (Button et al., 2013; Szucs and Ioannidis, 2017; Ioannidis et al., 2017).”
From With Little Power Comes Great Responsibility (2020) · cites this paper as Button et al., 2013
The cited source says what the claim attributes to it.
In the citing paper
“when significant effects are found, they are likely to be noisier and have lower positive predictive value (Button et al., 2013).”
From With Little Power Comes Great Responsibility (2020) · cites this paper as Button et al., 2013
The cited source says what the claim attributes to it.
In the citing paper
“Low power has three effects: first, within the experiment, real effects are more likely to be missed; second, when an effect is detected, this will often be an overestimation of the true effect size [24]; and finally, when low power is combined with publication bias, there is an increase in the false positive rate in the published literature [36].”
From Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0 (2020) · cites this paper as [36]
The cited source says what the claim attributes to it.
In the citing paper
“The reproducibility of scientific findings has been called into question.”
From Evaluating replicability of laboratory experiments in economics (2016) · cites this paper as [6]
In the citing paper
“Furthermore, sample sizes are often underpowered (4-6), at times leading to conclusions that do not hold up with larger-sized replications or metaanalyses (7).”
From Generic language in scientific communication (2019) · cites this paper as [5]
In the citing paper
“Testing more rather than fewer participants will make inferences more robust and more widely applicable as this can reduce random variation, increasing the probability that a statistically significant result reflects a true effect also likely to occur in retesting with similar groups [5].”
From Hasty generalizations and generics in medical research: A systematic review (2024) · cites this paper as [5]
In the citing paper
“Less clear, perhaps, is that low power can also increase the false discovery rate and the likelihood that reported effect sizes are inflated, due to their reduced ability to mute stochastic noise [13,63,64].”
From The natural selection of bad science (2016) · cites this paper as [63]
In the citing paper
“For most published research findings to be false in psychology and neuroscience, for example, one must assume that the hypotheses tested in these disciplines are correct much less than 50% of the time (14, 18).”
From Is science really facing a reproducibility crisis, and do we need it to? (2018) · cites this paper as [18]
In the citing paper
“Indeed, power analyses show that resting-state functional connectivity for individual links in people is not robust with small samples (Button et al., 2013; Poldrack, 2019), especially in populations with psychiatric disorders, as we also have found.”
From The orbitofrontal cortex: reward, emotion and depression (2020) · cites this paper as Button et al., 2013
In the citing paper
“For example, in neuroscience, even with lenient assumptions, the average power of an experiment is about 20%13,14.”
From How to design preclinical studies in nanomedicine and cell therapy to maximize the prospects of clinical translation (2018) · cites this paper as [13]
In the citing paper
“Overall the sample size was relatively small, which may limit reproducibility of results (Button et al., 2013), but was comparable to other relevant fMRI studies (Christakou et al., 2013, Richey et al., 2012, Rubia et al., 2009b).”
From Differentiating neural reward responsiveness in autism versus ADHD (2014) · cites this paper as Button et al., 2013
In the citing paper
“even if a lack of statistical power is a major cause of concern in neuroscience and may be imputed for several studies (Button et al., 2013)”
From What visual illusions teach us about schizophrenia (2014) · cites this paper as Button et al., 2013
In the citing paper
“Many studies included relatively small sample sizes potentially limiting the ability to observe true effects (Button et al., 2013).”
From Neural networks involved in adolescent reward processing: an activation likelihood estimation meta-analysis of functional neuroimaging studies (2015) · cites this paper as Button et al., 2013
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
Each contradiction between two passages listed here was confirmed by an independent check.
Detectable effect sizes are reversed for the two animal-study sample sizes
MinorThe animal-model section assigns d = 1.20 to n = 22 and d = 1.26 to n = 24 at 80% power. The ethical-implications section assigns the same effect sizes to the opposite sample sizes, changing which effect each typical study could detect.
“Studies of this size can only detect very large effects (d = 1.20 for n = 22, and d = 1.26 for n = 24) with 80% power”
“the average sample size of 22 animals for the water maze experiments was only sufficient to detect an effect size of d = 1.26 with Table 1 (cont.)| Characteristics of included meta-analyses Study k N Summary effect size Power Refs Median (range) Cohen's d OR Random or fixed effects Median (range) Yang 3 51 (18-205) 0.67 NA 0.65 (0.27-1.00) 67 Yuan 14 116.5 (19-1178) 4.98 Fixed 0.92”
Cited works that were retracted
Works this paper cites that were later retracted, from the retraction notices publishers deposit. Major when the notice came out before this paper was published.
Cites a retracted work: 10.1128/iai.05661-11
InfoThe cited work was retracted; the date of the notice is not on record.
Cited work 10.1128/iai.05661-11 · retraction date not known · this paper published 2013-04-10
“Fang, F. C. & Casadevall, A. Retracted science and the retraction index. Infect. Immun. 79, 3855–3859 (2011).”
Citations
Cites
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Retracted Science and the Retraction Index
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Flagged first, then by citations
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Ascending arousal network connectivity during recovery from traumatic coma
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“In studies performing brain-wide correlations between a neuroimaging feature and behavior, estimates of effect sizes inversely correlate with sample size, and small samples tend to produce noisy, unstable estimates (Lorca-Puls et al., 2018; Button et al., 2013; Marek et al., 2020).”
BackgroundLizarazu, M., di Covella, L. S., van Wassenhove, V., Rivière, D., Mizzi, R., Lehongre, K., Hertz‐Pannier, L., & Ramus, F. (2021) · Cortex · doi:10.1016/j.cortex.2020.12.024 · Cited by 41
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“51), it is important to note that large effect sizes in small samples may reflect inflated estimates of brainbehaviour associations [80, 81].”
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“The flip side is that studies with small samples are much noisier, which is why their results tend to not replicate (Button et al., 2013).”
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“The issue of replication has commanded massive recent attention (Anderson et al., 2016; Button et al., 2013; Gilbert, King, Pettigrew, & Wilson, 2016; Open Science, 2015), and we think this will do only good for science in the long run.”
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“Moreover, much of the published neuroimaging research uses small sample sizes (typically n <20), thus reducing the reliability of the data (19).”
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“In general, the results of this study should be interpreted with caution, given the small sample size that may lead to inflated effect sizes.(56) The number of individuals fulfilling depression criteria according to MINI decreased from 16 to 2 in the treatment group, and from 13 to 9 in the control group, which is pro…”
ResultHaddad, E., Pizzagalli, F., Zhu, A. H., Bhatt, R. R., Islam, T., Gari, I. B., Dixon, D., Thomopoulos, S. I., Thompson, P. M., & Jahanshad, N. (2022) · Human Brain Mapping · doi:10.1002/hbm.26147 · Cited by 50
“…of research findings in the biological sciences has recently come to light as a major problem, particularly for the neuroimaging-heavy fields of psychological and neurological-sciences (Boekel et al., 2015; Bowring et al., 2019; Button et al., 2013; Hodge et al., 2020; Poldrack et al., 2020).”
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“Low statistical power in EEG studies, caused by too few subjects, may also impact the reported results [92, 93].”
Creative females have larger white matter structures: Evidence from a large sample study
Takeuchi, H., Taki, Y., Nouchi, R., Yokoyama, R., Kotozaki, Y., Nakagawa, S., Sekiguchi, A., Iizuka, K., Yamamoto, Y., Hanawa, S., et al. (2016) · Human Brain Mapping · doi:10.1002/hbm.23369 · Cited by 48
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“Use of small sample sizes with insufficient power to detect GxE interactions has been a major point of criticism in GxE research for increasing risk of both false negative and false positive findings (Button et al., 2013).”
BackgroundTrautwein, F., Naranjo, J. R., & Schmidt, S. (2016) · Frontiers in Psychology · doi:10.3389/fpsyg.2016.01785 · Cited by 51
BRAITHWAITE, F., Walters, J. L., Li, L. S. K., Moseley, G. L., Williams, M., & McEvoy, M. P. (2018) · PeerJ · doi:10.7717/peerj.5318 · Cited by 44
“trials meant that there was insufficient evidence to determine if a moderating effect of blinding effectiveness or adequacy existed (Button et al., 2013; Higgins & Green, 2011).”
BackgroundMethodInfluentialLISA improves statistical analysis for fMRI
Lohmann, G., Stelzer, J., Lacosse, E., Kumar, V., Mueller, K., Kuehn, E., Grodd, W., & Scheffler, K. (2018) · Nature Communications · doi:10.1038/s41467-018-06304-z · Cited by 42
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Ethics in Scientific Research: An Examination of Ethical Principles and Emerging Topics
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“Last, given the challenges of small sample sizes (Button et al., 2013; Szucs and Ioannidis, 2017; Turner et al., 2018), high false-positive rates (Eklund et al., 2016; Szucs and Ioannidis, 2017), and lack of replicability in neuroimaging research (Ioannidis, 2005; Eklund et al., 2016; Szucs and…”
BackgroundCespón, J., Rodella, C., Miniussi, C., & Pellicciari, M. C. (2019) · Clinical Neurophysiology · doi:10.1016/j.clinph.2019.08.016 · Cited by 45
Parallel cortical-brainstem pathways to attentional analgesia
Oliva, V., Gregory, R., Davies, W.-E., Harrison, L., Moran, R., Pickering, A. E., & Brooks, J. C. W. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.117548 · Cited by 49
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Scan Once, Analyse Many: Using Large Open-Access Neuroimaging Datasets to Understand the Brain
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“Here I will focus on the availability of large-scale neuroimaging datasets that help us move beyond the statistical power issues that are still typical within the field (Button et al. 2013; Zuo et al. 2019) and more towards furthering our understanding of the brain.”
BackgroundDear reviewers: Responses to common reviewer critiques about infant neuroimaging studies
Korom, M., Camacho, M. C., Filippi, C. A., Licandro, R., Moore, L. A., Dufford, A. J., Zöllei, L., Graham, A. M., Spann, M. N., Howell, B., et al. (2021) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2021.101055 · Cited by 49
On the challenges of drawing conclusions from p -values just below 0.05
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“At the same time, we can be certain power varies substantially across studies and research disciplines (e.g., Button et al., 2013 ; Sedlmeier & Gigerenzer, 1989 ), and therefore the p -value distribution can be more accurately modeled by summing multiple p -value distributions across di ff erent…”
BackgroundThompson, P. M., Jahanshad, N., Ching, C. R. K., Salminen, L. E., Thomopoulos, S. I., Bright, J. K., Baune, B. T., Bertolín, S., Bralten, J., Bruin, W. B., et al. (2019) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/qnsh7 · Cited by 50
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“The lack of a rigorous plan results in the massive production of underpowered exploratory studies (Maxwell, 2004; Button et al., 2013; Lazic, 2018), with the aggravating factor that they are often misinterpreted as confirmatory studies ones (Wagenmakers et al.”
BackgroundThings you Wanted to Know about Bias in Evaluations but Never Dared to Think
Camfield, L., Duvendack, M., & Palmer‐Jones, R. (2014) · IDS Bulletin · doi:10.1111/1759-5436.12112 · Cited by 45
“These phenomena are also likely causes of the ‘decline effect’ when scientific claims receive decreasing support over time (Schooler 2011), and explain the need for replication studies to be more highly powered than the original (Button et al. 2013).”
BackgroundBeller, J., Kröger, C. B., & Hosser, D. (2019) · Journal of Interpersonal Violence · doi:10.1177/0886260519869071 · Cited by 44
“Yet, using the data set enabled us to study our research topic, the association between aspects of religiosity and support for honor violence, in a large, diverse, multinational, population-based sample, which is seen as pivotal to overcome the recent external validity crisis in empirical research (Button et al., 2013…”
BackgroundMethodThe Relationship between Working Memory and Arithmetic in Primary School Children: A Meta-Analysis
Zhang, Y., Tolmie, A. K., & Gordon, R. (2022) · Brain Sciences · doi:10.3390/brainsci13010022 · Cited by 66
“, increasing the margin of errors) due to the large variance [38].”
Analyzing Dataset Annotation Quality Management in the Wild
Klie, J.-C., de Castilho, R. E., & Gurevych, I. (2024) · Computational Linguistics · doi:10.1162/coli_a_00516 · Cited by 66
“and hence low statistical power, potentially leading to over-optimistic or incorrect conclusions (Button et al. 2013; Passonneau and Carpenter 2014).”
BackgroundFisher, P. M., Madsen, M. K., Mahon, B. M., Holst, K. K., Andersen, S. B., Laursen, H. R., Hasholt, L., Siebner, H. R., & Knudsen, G. M. (2013) · Biological Psychiatry · doi:10.1016/j.biopsych.2013.11.031 · Cited by 49
“Additionally, we suggest caution when interpreting our findings related to the 5-HTTLPR, as they, too, may be underpowered, which can increase risk for spurious findings (46).”
Scientific rigor and the art of motorcycle maintenance
Munafò, M., Noble, S., Browne, W. J., Brunner, D., Button, K., Ferreira, J., Holmans, P., Langbehn, D., Lewis, G., Lindquist, M., et al. (2014) · Nature Biotechnology · doi:10.1038/nbt.3004 · Cited by 43
Self-citationAssessing the effects of common variation in the FOXP2 gene on human brain structure
Hoogman, M., Guadalupe, T. M., Zwiers, M. P., Klarenbeek, P., Francks, C., & Fisher, S. E. (2014) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2014.00473 · Cited by 48
“…while common FOXP2 SNPs have been the subject of multiple neuroimaging genetics studies, all such investigations have involved notably small sample sizes with low power and high susceptibility to false positive findings (Button et al., 2013), and there are no reports of independent replications.”
BackgroundYagi, S., Drewczynski, D., Wainwright, S. R., Barha, C. K., Hershorn, O., & Galea, L. A. M. (2016) · Hormones and Behavior · doi:10.1016/j.yhbeh.2016.10.019 · Cited by 43
“It is therefore important to note that the environment (salience of cues in room) and training sessions (number,massed versus distributed)will influence the expression of sex differences and may be the reason why power calculations suggest very high numbers of animals are required to obtain sex differences in spatial…”
Transdiagnostic brain responses to disorder-related threat across four psychiatric disorders
Feldker, K., Heitmann, C. Y., Neumeister, P., Tupak, S. V., Schrammen, E., Moeck, R., Zwitserlood, P., Bruchmann, M., & Straube, T. (2016) · Psychological Medicine · doi:10.1017/s0033291716002634 · Cited by 45
“Statistical power was increased by a large patient sample, which makes the detection of true effects across diagnoses more likely (Rauch et al. 2003; Button et al. 2013).”
BackgroundCommon data elements and data management: Remedy to cure underpowered preclinical studies
Lapinlampi, N., Melin, E., Aronica, E., Bankstahl, J. P., Becker, A. J., Bernard, C., Gorter, J. A., Gröhn, O. H. J., Lipsanen, A., Łukasiuk, K., et al. (2016) · Epilepsy Research · doi:10.1016/j.eplepsyres.2016.11.010 · Cited by 43
The Cultural Context of Loneliness: Risk Factors in Active Duty Soldiers
Cacioppo, J. T., Cacioppo, S., Adler, A. B., Lester, P. B., McGurk, D., Thomas, J. L., & Chen, H. Y. (2016) · Journal of Social and Clinical Psychology · doi:10.1521/jscp.2016.35.10.865 · Cited by 51
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“As the study was small and underpowered, the risk for inflated effect sizes and type-I errors is also increased [34], which should be kept in mind when interpreting the findings.”
Consequences of relying on statistical significance: Some illustrations
CALSTER, B. V., Steyerberg, E. W., Collins, P. G. S., & Smits, T. (2018) · European Journal of Clinical Investigation · doi:10.1111/eci.12912 · Cited by 47
“effect sizes and surprisingly to many false positive conclusions, mainly in small to moderately sized studies [5,6,14-16].”
BackgroundResultKievit, R., Fuhrmann, D., Borgeest, G. S., Simpson-Kent, I. L., & Henson, R. N. (2018) · Wellcome Open Research · doi:10.12688/wellcomeopenres.14241.2 · Cited by 42
Briki, W., & Majed, L. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00252 · Cited by 45
Early-life social environment predicts social network position in wild zebra finches
Brandl, H. B., Farine, D. R., Funghi, C., Schuett, W., & Griffith, S. C. (2019) · Proceedings of the Royal Society B Biological Sciences · doi:10.1098/rspb.2018.2579 · Cited by 43
Carbine, K. A., & Larson, M. J. (2019) · Health Psychology Review · doi:10.1080/17437199.2019.1622144 · Cited by 44
Li, R., Smith, D. V., Clithero, J. A., Venkatraman, V., Carter, R. M., & Huettel, S. A. (2017) · Journal of Neuroscience · doi:10.1523/jneurosci.3486-16.2017 · Cited by 41
Neuroethics at 15: The Current and Future Environment for Neuroethics
Emerging Issues Task Force, I. N. S. (2019) · AJOB Neuroscience · doi:10.1080/21507740.2019.1632958 · Cited by 45
Media multitasking, mind-wandering, and distractibility: A large-scale study
Wiradhany, W., van Vugt, M. K., & Nieuwenstein, M. R. (2019) · Attention Perception & Psychophysics · doi:10.3758/s13414-019-01842-0 · Cited by 53
“Thus, a larger sample size and greater statistical power would be needed to be able to reliably detect these effects (Button et al., 2013).”
BackgroundFan, Y., Li, Z., Duan, X., Xiao, J., Guo, X., Han, S., Guo, J., Yang, S., Li, J., Cui, Q., et al. (2019) · Human Brain Mapping · doi:10.1002/hbm.24801 · Cited by 43
“Considering increasing concern about reproducibility of neuroscientific findings across different centers (Button et al., 2013), we additionally recruited an independent cohort (including 13 FESs and 13 HCs) from another center to examine the reproducibility of current findings.”
BackgroundPietschnig, J., Siegel, M., Eder, J. S. N., & Gittler, G. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.02874 · Cited by 52
Reproducibility of amygdala activation in facial emotion processing at 7T
Geissberger, N., Tik, M., Sladky, R., Woletz, M., Schuler, A., Willinger, D., & Windischberger, C. (2020) · NeuroImage · doi:10.1016/j.neuroimage.2020.116585 · Cited by 41
Rakesh, D., Allen, N. B., & Whittle, S. (2020) · Developmental Cognitive Neuroscience · doi:10.1016/j.dcn.2020.100775 · Cited by 44
Human in vitro models for understanding mechanisms of autism spectrum disorder
Gordon, A., & Geschwind, D. H. (2020) · Molecular Autism · doi:10.1186/s13229-020-00332-7 · Cited by 45
“Small sample sizes have lower power to detect changes, tend to overestimate effect sizes, and can lead to low reproducibility [160].”
BackgroundHolgado, D., Sanabria, D., Perales, J. C., & Vadillo, M. A. (2020) · Journal of Cognition · doi:10.5334/joc.126 · Cited by 44
Statistical Inference and the Replication Crisis
Colling, L. J., & Szűcs, D. (2019) · PhilPapers (PhilPapers Foundation) · doi:10.17863/cam.35452 · Cited by 41
Keating, C. T., Fraser, D. S., Sowden, S., & Cook, J. L. (2021) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-021-05083-9 · Cited by 48
Genetic underpinnings of risky behaviour relate to altered neuroanatomy
Aydogan, G., Daviet, R., Linnér, R. K., Hare, T. A., Kable, J. W., Kranzler, H. R., Wetherill, R. R., Ruff, C. C., Koellinger, P., & Nave, G. (2021) · Nature Human Behaviour · doi:10.1038/s41562-020-01027-y · Cited by 47
Melissa Bateson, B., Andrews, C. P., Dunn, J. C., Egger, C., Gray, F. E., Mchugh, M., & Nettle, D. (2021) · PeerJ · doi:10.7717/peerj.11541 · Cited by 44
Mind-wandering: mechanistic insights from lesion, tDCS, and iEEG
Kam, J. W. Y., Mittner, M., & Knight, R. T. (2022) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2021.12.005 · Cited by 44
Reger, M., Kutschan, S., Freuding, M., Schmidt, T., Josfeld, L., & Hübner, J. (2022) · Journal of Cancer Research and Clinical Oncology · doi:10.1007/s00432-022-03947-w · Cited by 47
Investigating the Relationship between Stable Personality Characteristics and Automatic Imitation
Butler, E. E., Ward, R., & Ramsey, R. (2015) · PLOS ONE · doi:10.1371/journal.pone.0129651 · Cited by 44
“In comparison to these prior studies, the current work tested a larger sample (2–3 times larger) and used an additional analytical approach (multiple regression) that could control for potentially confounding variables [26–28,31,57].”
BackgroundResultWhy bother using non-human primate models of cognitive disorders in translational research?
Camus, S. M. J., Ko, W. K. D., Pioli, E. Y., & Bézard, E. (2015) · Neurobiology of Learning and Memory · doi:10.1016/j.nlm.2015.06.012 · Cited by 45
The Place of Experimental Design and Statistics in the 3Rs
Parker, R. M., & Browne, W. J. (2014) · ILAR Journal · doi:10.1093/ilar/ilu044 · Cited by 49
“Indeed, given the chronic problem of underpowered studies, the need to reconsider how resources are best deployed is pressing (see Button et al. 2013 for a discussion).”
BackgroundResultInfluentialCipora, K., van Dijck, J., Georges, C., Masson, N., Goebel, S. M., Willmes, K., Pesenti, M., Schiltz, C., & Nuerk, H. (2019) · PsyArXiv (OSF Preprints) · doi:10.31234/osf.io/bwyr3 · Cited by 50
Frank, M. C. (2016) · doi:10.31234/osf.io/27b43 · Cited by 33
Efficacy of EEG neurofeedback in psychiatry: A comprehensive overview and meta-analysis
J.H. Begemann, M., J.R. Florisse, E., van Lutterveld, R., Kooyman, M., & E. Sommer, I. (2016) · Translational Brain Rhythmicity · doi:10.15761/tbr.1000105 · Cited by 44
“Underpowered studies carry the risk of both false positive and negative findings, and are more likely to be affected by publication bias, selective data analysis and selective reporting of outcomes [52].”
BackgroundPushing the Boundaries of Psychiatric Neuroimaging to Ground Diagnosis in Biology
Saggar, M., & Uddin, L. Q. (2019) · eneuro · doi:10.1523/eneuro.0384-19.2019 · Cited by 38
“…been reportedly going through a major replication crisis (Open Science Collab-oration, 2015; De Boeck and Jeon, 2018), and replicability of previously reported brain-behavior relations in neuro-imaging studies are also under heavy scrutiny (Button et al., 2013; Kharabian Masouleh et al., 2019).”
BackgroundIncreasing the Credibility of Political Science Research: A Proposal for Journal Reforms
Nyhan, B. J. (2015) · PS Political Science & Politics · doi:10.1017/s1049096515000463 · Cited by 57
“…self-deception—that is, as authors, reviewers, and editors, we are simply too practiced in rationalizing choices that produce the results we want, including “p-hacking” (Simmons, Nelson, and Simonsohn 2011; Simonsohn, Nelson, and Simmons 2013) and the use of low-power studies (Button et al. 2013).”
BackgroundMorales, K. F., Paget, J., & Spreeuwenberg, P. M. M. (2017) · BMC Infectious Diseases · doi:10.1186/s12879-017-2730-0 · Cited by 43
“However, it is well known from the statistical literature that such a small sample makes the analysis vulnerable to numerous problems and has significant limitations [62].”
BackgroundResearch Techniques Made Simple: Interpreting Measures of Association in Clinical Research
Roberts, M., Ashrafzadeh, S., & Asgari, M. M. (2019) · Journal of Investigative Dermatology · doi:10.1016/j.jid.2018.12.023 · Cited by 44
“Results from underpowered studies should also be interpreted with caution, because true associations may be masked by small sample size, or conversely, spurious, inflated risk estimates may be detected (Button et al., 2013).”
BackgroundNorman, L. J., Sudre, G. P., Price, J., Shastri, G. G., & Shaw, P. (2022) · Neuropsychopharmacology · doi:10.1038/s41386-022-01408-z · Cited by 56
“This is expected, as findings surviving significance in underpowered studies, even if they are true positives, are likely to be associated with inflated effect sizes [21, 30, 76].”
BackgroundHow reliable are amygdala findings in psychopathy? A systematic review of MRI studies
Deming, P., Heilicher, M. O., & Koenigs, M. R. (2022) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2022.104875 · Cited by 53
“The adverse effect of small sample sizes on study power and reproducibility has been widely recognized in the field of neuroimaging (Button et al., 2013; Poldrack et al., 2017; Szucs and Ioannidis, 2020; Marek et al., 2022; Cremers et al., 2017).”
van Dee, V., Schnack, H. G., & Cahn, W. (2023) · Schizophrenia Research · doi:10.1016/j.schres.2023.02.024 · Cited by 42
“We excluded case reports and case series with 20 or fewer included patients because of high risks of selection and publication bias, reduced chance of finding a true effect and reduced likelihood that a significant result reflects a true effect (Button et al., 2013).”
Mesquida, C., Murphy, J., Lakens, D., & Warne, J. P. (2023) · Journal of Sports Sciences · doi:10.1080/02640414.2023.2269357 · Cited by 57
“Thus, publication bias based on statistical significance and in the presence of studies with underpowered desgins leads to over-estimated effect size estimates (Bakker et al., 2012; Button et al., 2013; Anderson et al., 2017).”
BackgroundMethodResultInfluentialHarris, A. D., Puts, N. A. J., Anderson, B. A., Yantis, S., Pekar, J. J., Barker, P. B., & Edden, R. A. E. (2015) · PLoS ONE · doi:10.1371/journal.pone.0117531 · Cited by 43
“Small sample sizes can lead to insufficient statistical power and results being driving by a few data points or effect inflation [32].”
BackgroundHaram, M., Tesli, M., Bettella, F., Djurovic, S., Andreassen, O. A., & Melle, I. S. (2015) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2015.00009 · Cited by 43
“Or they could also be spurious findings due to small samples and low statistical power (Button et al., 2013) as indicated by the meta-analyses (Psychiatric Genomics Consortium (PGC), 2011; Bakermans-Kranenburg and van Ijzendoorn, 2014).”
Ylinen, S., & Kujala, T. (2015) · Frontiers in Psychology · doi:10.3389/fpsyg.2015.00137 · Cited by 43
“She argued that neuroscientific studies often use small subject groups, which may decrease their reliability and result in small statistical power (cf. Button et al., 2013).”
BackgroundMatheson, S. L., Shepherd, A. M., & Carr, V. J. (2014) · Psychological Medicine · doi:10.1017/s0033291714000166 · Cited by 42
“Further, summary effects from meta-analyses may be modestly inflated (Button et al. 2013).”
BackgroundAutomatic imitation is reduced in narcissists.
Obhi, S. S., Hogeveen, J., Giacomin, M., & Jordan, C. H. (2013) · Journal of Experimental Psychology Human Perception & Performance · doi:10.1037/a0034056 · Cited by 47
Reward modulation of cognitive function in adult attention-deficit/hyperactivity disorder
Aarts, E., van Holstein, M., Hoogman, M., Onnink, A. M. H., Kan, C. C., Franke, B., Buitelaar, J. K., & Cools, R. (2014) · Behavioural Pharmacology · doi:10.1097/fbp.0000000000000116 · Cited by 45
“It is unlikely that our whole-brain corrected results represent a false-positive effect as our power calculation based on an independent dataset (Aarts et al., 2010; Button et al., 2013) confirmed that our sample should be large enough to obtain significantly meaningful effects (see the Methods section).”
BackgroundInfluentialSimilar white matter but opposite grey matter changes in schizophrenia and high‐functioning autism
Katz, J., d’Albis, M.-A., Boisgontier, J., Poupon, C., Mangin, J.-F., Guevara, P., Duclap, D., Hamdani, N., Petit, J., Monnet, D., et al. (2016) · Acta Psychiatrica Scandinavica · doi:10.1111/acps.12579 · Cited by 48
“The relatively small sample induces a low statistical power and a risk of overestimating effect sizes (51).”
BackgroundArnold, P. A., Cassey, P., & White, C. R. (2016) · Functional Ecology · doi:10.1111/1365-2435.12772 · Cited by 45
“may undermine identification of the true relationship (Button et al. 2013).”
BackgroundKishi, T., Oya, K., & Iwata, N. (2016) · Psychiatry Research · doi:10.1016/j.psychres.2016.10.053 · Cited by 43
Zeiss, C. J., Allore, H. G., & Beck, A. P. (2017) · PLoS ONE · doi:10.1371/journal.pone.0171790 · Cited by 46
Analyzing crowdsourced ratings of speech-based take-over requests for automated driving
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Ali, M. A., Hirshman, B. R., Wilson, B. R., Carroll, K. T., Proudfoot, J. A., Goetsch, S. J., Alksne, J. F., Ott, K. H., Aiyama, H., Nagano, O., et al. (2017) · World Neurosurgery · doi:10.1016/j.wneu.2017.07.062 · Cited by 44
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Tutorial: Small-N Power Analysis
Kyonka, E. G. E. (2018) · Perspectives on Behavior Science · doi:10.1007/s40614-018-0167-4 · Cited by 52
“It also does not mean that if the result is statistically significant, the strategy is guaranteed to be costeffective (Button et al., 2013).”
BackgroundEffect Sizes, Power, and Biases in Intelligence Research: A Meta-Meta-Analysis
Nuijten, M. B., van Assen, M. A. L. M., Augusteijn, H. E. M., Crompvoets, E. A. V., & Wicherts, J. M. (2020) · Journal of Intelligence · doi:10.3390/jintelligence8040036 · Cited by 42
“…that sample sizes in psychology research are typically too small to detect the expected true effect sizes (which are also estimated to be small to medium), leading to the conclusion that many psychology studies may be underpowered (Button et al. 2013; Cohen 1962; Sedlmeier and Gigerenzer 1989).”
BackgroundResultInfluentialThe antidepressant impact of minocycline in rodents: A systematic review and meta-analysis
Reis, D. J., Casteen, E. J., & Ilardi, S. S. (2019) · Scientific Reports · doi:10.1038/s41598-018-36507-9 · Cited by 45
Erskine, A., Bus, T., Herb, J. T., & Schaefer, A. T. (2019) · PLoS ONE · doi:10.1371/journal.pone.0211571 · Cited by 49
Rowe, A., Titterington, J., Holmes, J., Henry, L. A., & Taggart, L. (2019) · Developmental Review · doi:10.1016/j.dr.2019.02.001 · Cited by 50
“Fewer than 50% of included studies were randomised controlled trials ( n = 8) and none of the studies reported un-dertaking a power analysis, so small sample sizes may have resulted in inflated effects being reported (Button et al., 2013).”
BackgroundHow, M.-L., & Hung, D. W. L. (2019) · Education Sciences · doi:10.3390/educsci9020110 · Cited by 53
“or require normal distributions as the underlying parameters of a model [10,16,17].”
Background
Metadata
- Authors
- Button, K. S., Ioannidis, J. P. A., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S. J., & Munafò, M. R.
- Affiliations
- University of Bristol (GB)
- Stanford Medicine (US)
- University of Virginia (US)
- Venue
- Nature Reviews Neuroscience
- Published
- 2013-04-10
- Volume
- 14
- Issue
- 5
- Pages
- 365-376
- ISSN
- 1471-003X (ISSN-L)
- 1471-0048
- OpenAlex
- W2105824687
- Semantic Scholar
- c2bc880156c10fc8d1752cd3d528e4ef96cbaac4
- Cited by
- 7,641
- References
- 106
- Citations by year
- 2026: 584 · 2025: 611 · 2024: 606 · 2023: 553 · 2022: 658 · 2021: 692 · 2020: 752 · 2019: 683 · 2018: 679 · 2017: 597 · 2016: 486 · 2015: 352 · 2014: 268 · 2013: 109
- FWCI
- 82.23
- Type
- journal article
- Language
- en
- Update notices
- erratum (2013-04-15)
- Open access
- bronze
- doi.org
- Locations
- Nature reviews. Neuroscience (publishedVersion)
- PubMed (publishedVersion)
- Explore Bristol Research (submittedVersion)
- UCL Discovery (University College London) (submittedVersion)
- Funders
- Wellcome Trust (090532)
- United Kingdom Clinical Research Collaboration
- Cancer Research UK
- National Institute for Health and Care Research
- British Heart Foundation
- Medical Research Council
- Economic and Social Research Council
- Topics
- Social Sciences › Decision Sciences › Statistics, Probability and Uncertainty › Meta-analysis and systematic reviews
- Social Sciences › Psychology › Experimental and Cognitive Psychology › Psychology Research and Bibliometrics
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods in Epidemiology
- Keywords
- statistical power, small sample size, effect size overestimation, reproducibility, research ethics, research waste, research practices, brain research
- MeSH terms
- Humans, Neurosciences, Probability, Reproducibility of Results, Sample Size
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 2026-10-11, under version 3 of the claim definition (model deepseek-v4.1-flash, prompt extract-v3).
A second model labelled each claim on 2026-10-11 (model jev-1.13-20260917, definition revision gold-guideline-v0.2).
Cited sources are checked with a language model’s help (gpt-6-sol).
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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