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
45 Own interpretation · 15 No basis given · 12 Own result · 9 No full text of the sources · 5 Partly supported · 4 Sources not found · 3 Supported · 3 Supported so far · 3 Own method · 1 Not checked yet · 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)
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.
- 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)
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.
- 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
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.
- 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)
boostedcorrelational- 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.
- 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)
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.
The 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)
plain- 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.
- 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.
hedgedn = 730- 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.
- 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
hedgedn = 461- 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.
None 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
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 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.
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.
- 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
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.
- 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)
hedged- 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.
- 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)
hedgedcorrelational- 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.
- 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.
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.
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
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.
- 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.
hedged- 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 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.
plainn = 22- 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.
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.
hedgeddirect- 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 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.
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.
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
plaincorrelationaln = 7- 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 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
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.
Probably 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
hedgedconditional- 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
“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.
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.
It 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.
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. 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.
plain- 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.
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.
plaindirect- Asserted: passes — The authors put it forward as true.
- Checkable: fails — It may be too vague to check 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 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.
plain- 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
“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.
plaincorrelational- 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.
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
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.
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
“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.
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.
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.
plain- 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.
- 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
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
“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.
hedgedconditional- 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.
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
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.
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
“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
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.
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
hedgedcorrelational- 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.
- 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.
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.
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
“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
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.
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
“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.
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 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
“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
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.
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
“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.
hedgeddirect- 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
“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
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. 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
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.
It 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.
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.
Probably 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.
Rated minor: it would not touch a result — a value no result rests on, a p-value that stays on the same side of the significance level, or a conflict that is clerical, resolved elsewhere in the paper, or outside its own findings.
In the paper
§ Power failure: why small sample size undermines the reliability of neuroscience > Animal model studies.
“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”
§ Power failure: why small sample size undermines the reliability of neuroscience > Ethical implications.
“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.
A note, not an issue: for example, a cited work that was retracted only after this paper was published, which its authors could not have known.
In the paper
“Fang, F. C. & Casadevall, A. Retracted science and the retraction index. Infect. Immun. 79, 3855–3859 (2011).”
Cited work 10.1128/iai.05661-11 · retraction date not known · this paper published 2013-04-10
Citations
Cites
1 retracted
Retracted Science and the Retraction Index
Fang, F. C., & Casadevall, A. (2011) · Infection and Immunity · doi:10.1128/iai.05661-11 · Cited by 405
RetractedFaul, F., Erdfelder, E., Lang, A.-G., & Buchner, A. (2007) · Behavior Research Methods · doi:10.3758/bf03193146 · Cited by 65,971
Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · PLoS Medicine · doi:10.1371/journal.pmed.0020124 · Cited by 10,505
Self-citationSterne, J. A. C., Sutton, A. J., Ioannidis, J. P. A., Terrin, N., Jones, D. R., Lau, J., Carpenter, J., Rucker, G., Harbord, R. M., Schmid, C. H., et al. (2011) · BMJ · doi:10.1136/bmj.d4002 · Cited by 7,709
Self-citationImproving Bioscience Research Reporting: The ARRIVE Guidelines for Reporting Animal Research
Kilkenny, C., Browne, W. J., Cuthill, I. C., Emerson, M., & Altman, D. G. (2010) · PLoS Biology · doi:10.1371/journal.pbio.1000412 · Cited by 7,300
Simmons, J. P., Nelson, L. D., & Simonsohn, U. (2011) · Psychological Science · doi:10.1177/0956797611417632 · Cited by 6,567
Improving bioscience research reporting: The ARRIVE guidelines for reporting animal research
Kilkenny, C., Browne, W. J., Cuthill, I. C., Emerson, M., & Altman, D. G. (2010) · Journal of Pharmacology and Pharmacotherapeutics · doi:10.4103/0976-500x.72351 · Cited by 6,277
The Principles of Humane Experimental Technique
(1960) · The Medical Journal of Australia · doi:10.5694/j.1326-5377.1960.tb73127.x · Cited by 5,045
Why Most Published Research Findings Are False
Ioannidis, J. P. A. (2005) · CHANCE · doi:10.1080/09332480.2005.10722754 · Cited by 4,702
Self-citationBelief in the law of small numbers.
Tversky, A., & Kahneman, D. (1971) · Psychological Bulletin · doi:10.1037/h0031322 · Cited by 3,246
Research works printed but not linked: 2
- David, S. P. et al. Potential reporting bias in small fMRI studies of the brain. PLoS Biol. (in the press).
- Ioannidis, J. P., Tarone, R. & McLaughlin, J. K. The false-positive to false-negative ratio in epidemiologic studies. Epidemiology 22, 450–456 (2011). · DOI · Paper page
Cited by
Sorted by citations
Estimating the reproducibility of psychological science
Open Science Collaboration (2015) · Science · doi:10.1126/science.aac4716 · Cited by 8,515
“Low-powerresearchdesignscombinedwithpublication bias favoring positive results together produce a literature with upwardly biased effect sizes ( 14 , 16 , 33 , 34 ).”
Cluster failure: why fMRI inferences for spatial extent have inflated false-positive rates
Eklund, A., Nichols, T. E., & Knutsson, H. (2016) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1602413113 · Cited by 3,487
“In addition to unreliable statistical methods, the neuroimaging field also suffers from studies having low statistical power [42, 43].”
MethodPermutation inference for the general linear model
Winkler, A. M., Ridgway, G. R., Webster, M. A., Smith, S. M., & Nichols, T. E. (2014) · NeuroImage · doi:10.1016/j.neuroimage.2014.01.060 · Cited by 3,563
“There are many reasonswhy larger samples aremore appropriate (see Button et al. (2013) for a recent review), and inwhat concerns permutation methods, larger samples allow smaller p-values, improve the variance estimates for each VG (which are embodied in the weighting matrix under restricted exchangeability), and allo…”
BackgroundA manifesto for reproducible science
Munafò, M. R., Nosek, B. A., Bishop, D. V. M., Button, K. S., Chambers, C. D., Percie du Sert, N., Simonsohn, U., Wagenmakers, E.-J., Ware, J. J., & Ioannidis, J. P. A. (2017) · Nature Human Behaviour · doi:10.1038/s41562-016-0021 · Cited by 3,397
“Studies of statistical power persistently find it to be below (sometimes well below) 50%, across both time and the different disciplines studied 2,35,36 .”
Self-citationStatistical tests, P values, confidence intervals, and power: a guide to misinterpretations
Greenland, S., Senn, S. J., Rothman, K. J., Carlin, J. B., Poole, C., Goodman, S. N., & Altman, D. G. (2016) · European Journal of Epidemiology · doi:10.1007/s10654-016-0149-3 · Cited by 3,200
Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0
Percie du Sert, N., Ahluwalia, A., Alam, S., Avey, M. T., Baker, M., Browne, W. J., Clark, A., Cuthill, I. C., Dirnagl, U., Emerson, M., et al. (2020) · PLOS Biology · doi:10.1371/journal.pbio.3000411 · Cited by 2,677
Bakdash, J. Z., & Marusich, L. R. (2017) · Frontiers in Psychology · doi:10.3389/fpsyg.2017.00456 · Cited by 2,067
“Low power typically overestimates effect sizes (e.g., Button et al., 2013).”
BackgroundUsing Bayes to get the most out of non-significant results
Dienes, Z. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00781 · Cited by 1,998
“Cohen (1962), Sedlmeier and Gigerenzer (1989); see Button et al. (2013), for a considerably lower recent estimate in some disciplines.”
BackgroundReproducible brain-wide association studies require thousands of individuals
Marek, S. A., Tervo‐Clemmens, B., Calabro, F. J., Montez, D. F., Kay, B. P., Hatoum, A. S., Donohue, M. R., Foran, W., Miller, R. L., Hendrickson, T. J., et al. (2022) · Nature · doi:10.1038/s41586-022-04492-9 · Cited by 2,275
Lakens, D. (2022) · Collabra Psychology · doi:10.1525/collabra.33267 · Cited by 1,962
Friedman, N. P., & Miyake, A. (2017) · Cortex · doi:10.1016/j.cortex.2016.04.023 · Cited by 1,834
Meta-analysis and the science of research synthesis
Gurevitch, J., Koricheva, J., Nakagawa, S., & Stewart, G. (2018) · Nature · doi:10.1038/nature25753 · Cited by 1,709
Bayesian inference for psychology. Part I: Theoretical advantages and practical ramifications
Wagenmakers, E., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Love, J., Selker, R., Gronau, Q. F., Šmíra, M., Epskamp, S., et al. (2017) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1343-3 · Cited by 1,767
The reliability paradox: Why robust cognitive tasks do not produce reliable individual differences
Hedge, C., Powell, G., & Sumner, P. (2018) · Behavior Research Methods · doi:10.3758/s13428-017-0935-1 · Cited by 1,457
Scanning the horizon: towards transparent and reproducible neuroimaging research
Poldrack, R. A., Baker, C. I., Durnez, J., Gorgolewski, K. J., Matthews, P. M., Munafò, M. R., Nichols, T. E., Poline, J.-B., Vul, E., & Yarkoni, T. (2017) · Nature Reviews Neuroscience · doi:10.1038/nrn.2016.167 · Cited by 1,410
Self-citationVan Dam, N. T., van Vugt, M. K., Vago, D. R., Schmalzl, L., Saron, C. D., Olendzki, A., Meissner, T., Lazar, S. W., Kerr, C. E., Gorchov, J., et al. (2018) · Perspectives on Psychological Science · doi:10.1177/1745691617709589 · Cited by 1,506
“…both psychological science and wider swaths of other basic and applied sciences, receiving considerable attention in both the scientific literature (Button et al., 2013; Ioannidis, 2005, 2012; Miguel et al., 2014; Open Science Collaboration, 2012; Pashler & Wagenmakers, 2012) and public news media…”
InfluentialCamerer, C. F., Dreber, A., Holzmeister, F., Ho, T.-H., Huber, J., Johannesson, M., Kirchler, M., Nave, G., Nosek, B. A., Pfeiffer, T., et al. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0399-z · Cited by 1,521
Self-citationPower Analysis and Effect Size in Mixed Effects Models: A Tutorial
Brysbaert, M., & Stevens, M. (2018) · Journal of Cognition · doi:10.5334/joc.10 · Cited by 1,420
Evaluating replicability of laboratory experiments in economics
Camerer, C. F., Dreber, A., Forsell, E., Ho, T.-H., Huber, J., Johannesson, M., Kirchler, M., Almenberg, J., Altmejd, A., Chan, T., et al. (2016) · Science · doi:10.1126/science.aaf0918 · Cited by 1,314
Beyond Power Calculations: Assessing Type S (Sign) and Type M (Magnitude) Errors
Gelman, A., & Carlin, J. (2014) · Perspectives on Psychological Science · doi:10.1177/1745691614551642 · Cited by 1,343
“Using statistical significance as a screener can lead researchers to drastically overestimate the magnitude of an effect (Button et al., 2013).”
BackgroundThe association of cigarette smoking with depression and anxiety: a systematic review
Fluharty, M., Taylor, A. E., Grabski, M., & Munafò, M. R. (2017) · Nicotine & Tobacco Research · doi:10.1093/ntr/ntw140 · Cited by 1,299
Self-citationHow to get statistically significant effects in any ERP experiment (and why you shouldn't)
Luck, S. J., & Gaspelin, N. (2016) · Psychophysiology · doi:10.1111/psyp.12639 · Cited by 1,202
“Estimating how often this happens is difficult, but there is growing evidence that many published results in psychology (Open Science Collaboration, 2015), neuroscience (Button et al., 2013), and oncology (Prinz, Schlange, & Asadullah, 2011) are not replicable.”
BackgroundDo “brain-training” programs work?
Simons, D. J., Boot, W. R., Charness, N., Gathercole, S. E., Chabris, C. F., Hambrick, D. Z., & Stine-Morrow, E. A. L. (2016) · Psychological Science in the Public Interest · doi:10.1177/1529100616661983 · Cited by 1,183
“Many of the studies tested small samples, limiting their ability to detect small effects and increasing the chances that any observed effects would be spurious (Button et al., 2013).”
BackgroundReproducibility in Science: Improving the Standard for Basic and Preclinical Research
Begley, C. G., & Ioannidis, J. P. A. (2015) · Circulation Research · doi:10.1161/circresaha.114.303819 · Cited by 1,035
“…Where Concerns Regarding Reproducibility Have Been Raised Discipline Issues Raised Author Neuroscience Low statistical power; small sample size Button et al (2013)36 Pharmacology Lack of training, lack of statistical power, blinding, hypothesis, requisite PK studies, randomization,…”
Self-citationMany Labs 2: Investigating variation in replicability across samples and settings
Klein, R. A., Vianello, M., Hasselman, F., Adams, B. G., Adams, R. B., Alper, S., Aveyard, M., Axt, J. R., Babalola, M. T., Bahník, Š., et al. (2018) · Advances in Methods and Practices in Psychological Science · doi:10.1177/2515245918810225 · Cited by 944
“They can also occur because of pervasive lowpowered research plus publication bias that favors positive over negative results (Button et al., 2013; Cohen, 1962; Greenwald, 1975; Rosenthal, 1979) and because of questionable research practices, such as p-hacking, that can inflate the likelihood of obtaining false positi…”
BackgroundSelf-citationStudying and modifying brain function with non-invasive brain stimulation
Polanía, R., Nitsche, M. A., & Ruff, C. C. (2018) · Nature Neuroscience · doi:10.1038/s41593-017-0054-4 · Cited by 1,090
Experimental design and analysis and their reporting: new guidance for publication in BJP
Curtis, M. J., Bond, R. A., Spina, D., Ahluwalia, A., Alexander, S. P. H., Giembycz, M. A., Gilchrist, A., Hoyer, D., Insel, P. A., Izzo, A. A., et al. (2015) · British Journal of Pharmacology · doi:10.1111/bph.12856 · Cited by 1,057
“False positives can arise more often when a study has not been randomized, and/or is grossly underpowered (Button et al., 2013), but false positives may nevertheless arise from chance alone.”
BackgroundIs psychology suffering from a replication crisis? What does “failure to replicate” really mean?
Maxwell, S. E., Lau, M. Y., & Howard, G. S. (2015) · American Psychologist · doi:10.1037/a0039400 · Cited by 1,102
“Similarly, we agree that the continuation of underpowered studies in many areas of psychology (e.g., Button et al., 2013) undermines scientific psychology.”
BackgroundThe oxytocin receptor: from intracellular signaling to behavior
Jurek, B., & Neumann, I. D. (2018) · Physiological Reviews · doi:10.1152/physrev.00031.2017 · Cited by 999
“, invalid statistical means and biased publication, also exist in other fields of psychology (48, 937) or neuroscience in general (141), the increasing number of studies on the overwhelming effects of i.”
BackgroundHypothermia versus Normothermia after Out-of-Hospital Cardiac Arrest
Dankiewicz, J., Cronberg, T., Lilja, G., Jakobsen, J. C., Levin, H., Ullén, S., Rylander, C. C., Wise, M. P., Oddo, M., Cariou, A., et al. (2021) · New England Journal of Medicine · doi:10.1056/nejmoa2100591 · Cited by 1,008
The Statistical Crisis in Science
Gelman, A., & Loken, E. (2014) · American Scientist · doi:10.1511/2014.111.460 · Cited by 963
The neuroendocrinology of social isolation
Cacioppo, J. T., Cacioppo, S., Capitanio, J. P., & Cole, S. W. (2015) · Annual Review of Psychology · doi:10.1146/annurev-psych-010814-015240 · Cited by 977
“As Button et al. (2013) detail, a small sample size reduces the likelihood of detecting a true effect (due to low statistical power), increases the likelihood that the effect size of a true effect is overestimated (due to the use of p < 0.05 to identify when an effect has been “detected” and the…”
BackgroundInfluentialThe natural selection of bad science
Smaldino, P. E., & McElreath, R. (2016) · Royal Society Open Science · doi:10.1098/rsos.160384 · Cited by 860
“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].”
BackgroundWestfall, J., Kenny, D. A., & Judd, C. M. (2014) · Journal of Experimental Psychology: General · doi:10.1037/xge0000014 · Cited by 928
“…salient in light of the growing awareness in psychology and other experimental sciences of the problems created and maintained by a proliferation of chronically underpowered studies (Asendorpf et al., 2013; Bakker, van Dijk, & Wicherts, 2012; Button et al., 2013; Ioannidis, 2008; Schimmack, 2012).”
BackgroundYan, C.-G., Chen, X., Li, L., Castellanos, F. X., Bai, T.-J., Bo, Q.-J., Cao, J., Chen, G.-M., Chen, N.-X., Chen, W., et al. (2019) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1900390116 · Cited by 895
“Inconsistencies may reflect limited statistical power (2) from small samples, but data analysis flexibility may also contribute, as a large number of preprocessing and analysis operations with many different parameter combinations have been used in fMRI analyses (11).”
Cross-validation failure: Small sample sizes lead to large error bars
Varoquaux, G. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.06.061 · Cited by 897
“The effects that I describe are related to the “power failure” of Button et al. (2013): lack of statistical power.”
BackgroundPrecuneus Is a Functional Core of the Default-Mode Network
Utevsky, A. V., Smith, D. V., & Huettel, S. A. (2014) · Journal of Neuroscience · doi:10.1523/jneurosci.4227-13.2014 · Cited by 925
Presence and Cybersickness in Virtual Reality Are Negatively Related: A Review
Weech, S., Kenny, S., & Barnett-Cowan, M. (2019) · Frontiers in Psychology · doi:10.3389/fpsyg.2019.00158 · Cited by 913
“In the single case where an a priori power analysis was conducted, a desired power of 80%, which is on the low-end of “adequate” (Button et al., 2013), requires approximately 85 participants.”
BackgroundMelby-Lervåg, M., Redick, T. S., & Hulme, C. (2016) · Perspectives on Psychological Science · doi:10.1177/1745691616635612 · Cited by 983
“Given the tendency for small sample sizes to produce inflated effect-size estimates (Button et al., 2013), future studies should include larger samples to produce more precise effect-size estimates.”
Sex Differences in the Adult Human Brain: Evidence from 5216 UK Biobank Participants
Ritchie, S. J., Cox, S. R., Shen, X., Lombardo, M. V., Reus, L. M., Alloza, C., Harris, M. A., Alderson, H. L., Hunter, S., Neilson, E., et al. (2018) · Cerebral Cortex · doi:10.1093/cercor/bhy109 · Cited by 855
“Given the surfeit of small-n studies in neuroscience (Button et al. 2013; Nord et al. 2017), it is of great importance to test hypotheses in large, well-powered samples, especially given that many neural sex differences are modest in size (Joel et al.”
BackgroundPost-traumatic stress disorder.
Yehuda, R., Hoge, C. W., McFarlane, A. C., Vermetten, E., Lanius, R. A., Nievergelt, C. M., Hobfoll, S. E., Koenen, K. C., Neylan, T. C., & Hyman, S. E. (2015) · Nature Reviews Disease Primers · doi:10.1038/nrdp.2015.57 · Cited by 870
The ENIGMA Consortium: large-scale collaborative analyses of neuroimaging and genetic data
the Alzheimer’s Disease Neuroimaging Initiative, E. C., Thompson, P. M., Stein, J. L., Medland, S. E., Hibar, D. P., Vasquez, A. A., Renteria, M. E., Toro, R., Jahanshad, N., Schumann, G., et al. (2014) · Brain Imaging and Behavior · doi:10.1007/s11682-013-9269-5 · Cited by 838
“Also, the sample sizes involved—unprecedented for a neuroimaging study—alleviate some of the concerns about underpowered studies and unreliable findings (Button et al. 2013).”
Effect Size Guidelines, Sample Size Calculations, and Statistical Power in Gerontology
Brydges, C. R. (2019) · Innovation in Aging · doi:10.1093/geroni/igz036 · Cited by 826
Ten simple rules for neuroimaging meta-analysis
Müller, V. I., Cieslik, E. C., Laird, A. R., Fox, P. T., Radua, J., Mataix-Cols, D., Tench, C. R., Yarkoni, T., Nichols, T. E., Turkeltaub, P. E., et al. (2018) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2017.11.012 · Cited by 805
“However, single imaging studies usually rely on underpowered studies with small sample sizes, which leads to many missed results (Button et al., 2013) and pushes researchers towards analyses and thresholding procedures that increase false positives (Eklund et al., 2016; Wager et al., 2007; Wager et…”
BackgroundSmall Telescopes: Detectability and the Evaluation of Replication Results
Simonsohn, U. (2015) · Psychological Science · doi:10.1177/0956797614567341 · Cited by 742
“If the original study is powered to 50%, an optimistic scenario (Button et al., 2013; Cohen, 1962; Rossi, 1990; Sedlmeier & Gigerenzer, 1989), the resulting upward bias in effect size would be severe enough that replications with samples sizes set to obtain 80% power, given the reported effects, would on average have…”
A Gentle Introduction to Bayesian Analysis: Applications to Developmental Research
van de Schoot, R., Kaplan, D. E., Denissen, J. J. A., Asendorpf, J. B., Neyer, F. J., & van Aken, M. A. G. (2013) · Child Development · doi:10.1111/cdev.12169 · Cited by 859
“When the sample size is small, it is often hard to attain statistical signi fi cant or meaningful results (e.g., Button, et al., 2013).”
Explicit and implicit contributions to learning in a sensorimotor adaptation task
Taylor, J. A., Krakauer, J. W., & Ivry, R. B. (2014) · The Journal of Neuroscience · doi:10.1523/jneurosci.3619-13.2014 · Cited by 768
“Thus, to have sufficient power to detect significant effects, even if the effects are slightly smaller than in the previous datasets, we recruited 15 participants for each of the four groups (Button et al., 2013).”
ResultReplicability, Robustness, and Reproducibility in Psychological Science
Nosek, B. A., Hardwicke, T. E., Moshontz, H., Allard, A., Corker, K. S., Dreber, A., Fidler, F., Hilgard, J., Kline Struhl, M., Nuijten, M. B., et al. (2022) · Annual Review of Psychology · doi:10.1146/annurev-psych-020821-114157 · Cited by 770
Self-citationThe fickle P value generates irreproducible results
Halsey, L. G., Curran‐Everett, D., Vowler, S. L., & Drummond, G. B. (2015) · Nature Methods · doi:10.1038/nmeth.3288 · Cited by 787
Bayes factor design analysis: Planning for compelling evidence
Schönbrodt, F. D., & Wagenmakers, E. (2017) · Psychonomic Bulletin & Review · doi:10.3758/s13423-017-1230-y · Cited by 810
“Specifically, underpowered studies inflate both false-negative and false-positive results (Button et al., 2013; Dreber et al., 2015; Ioannidis, 2005; Lakens & Evers, 2014), wasting valuable resources such as the time and effort of participants, the lives of animals, and scientific funding provided by society.”
ResultSzucs, D., & Ioannidis, J. P. A. (2017) · PLOS Biology · doi:10.1371/journal.pbio.2000797 · Cited by 761
Self-citationp -Curve and Effect Size: Correcting for Publication Bias Using Only Significant Results
Simonsohn, U., Nelson, L. D., & Simmons, J. P. (2014) · Perspectives on Psychological Science · doi:10.1177/1745691614553988 · Cited by 717
Registered Reports: A Method to Increase the Credibility of Published Results
Nosek, B. A., & Lakens, D. (2014) · Social Psychology · doi:10.1027/1864-9335/a000192 · Cited by 716
“Therefore, not finding a predicted effect in a large study may indicate more about the likelihood that an effect is true than finding a predicted effect in a small study , because the former is statistically less likely if an effect is true (Button et al., 2013; Lakens & Evers, in press).”
BackgroundSelf-citationDeriving reproducible biomarkers from multi-site resting-state data: An Autism-based example
Abraham, A., Milham, M. P., Di Martino, A., Craddock, R. C., Samaras, D., Thirion, B., & Varoquaux, G. (2017) · NeuroImage · doi:10.1016/j.neuroimage.2016.10.045 · Cited by 701
“Using larger datasets is commonly cited as a solution to challenges in reproducibility and statistical power (Button et al., 2013).”
BackgroundEickhoff, S. B., Nichols, T. E., Laird, A. R., Hoffstaedter, F., Amunts, K., Fox, P. T., Bzdok, D., & Eickhoff, C. R. (2016) · NeuroImage · doi:10.1016/j.neuroimage.2016.04.072 · Cited by 683
“Moreover, it may be discussed whether indeed something like “the” true location of an effect exists, given recent accounts of distributed coding and representation (Bzdok et al., 2015; Haxby, 2012; Kriegeskorte, 2009; Rissman and Wagner, 2012).”
Psychology, Science, and Knowledge Construction: Broadening Perspectives from the Replication Crisis
Shrout, P. E., & Rodgers, J. L. (2018) · Annual Review of Psychology · doi:10.1146/annurev-psych-122216-011845 · Cited by 830
“Some have recommended u s e on l y increased sample sizes (Button et al. 2013, Francis 2012a, Maxwell et al. 2015, Perugini 2014), and some have even suggested specific target attention to measurement error (Asendorpf et al. 2013) and to the quality of peer review (Coyne 2016, Nosek & Bar-Anan F o r…”
BackgroundBest practices in data analysis and sharing in neuroimaging using MRI
Nichols, T. E., Das, S., Eickhoff, S. B., Evans, A. C., Glatard, T., Hanke, M., Kriegeskorte, N., Milham, M. P., Poldrack, R. A., Poline, J.-B., et al. (2017) · Nature Neuroscience · doi:10.1038/nn.4500 · Cited by 714
Enhancing studies of the connectome in autism using the autism brain imaging data exchange II
Martino, A. D., O’Connor, D. A., Chen, B., Alaerts, K., Anderson, J. S., Assaf, M., Balsters, J. H., Baxter, L. C., Beggiato, A., Bernaerts, S., et al. (2017) · Scientific Data · doi:10.1038/sdata.2017.10 · Cited by 713
Elliott, M. L., Knodt, A. R., Ireland, D., Morris, M. L., Poulton, R., Ramrakha, S., Sison, M. L., Moffitt, T. E., Caspi, A., & Hariri, A. R. (2020) · Psychological Science · doi:10.1177/0956797620916786 · Cited by 714
“It has been suggested that neuroscience is generally an underpowered enterprise and that small sample sizes undermine fMRI research in particular (Button et al., 2013).”
Global negative effects of nitrogen deposition on soil microbes
Zhang, T., Chen, H. Y. H., & Ruan, H. (2018) · The ISME Journal · doi:10.1038/s41396-018-0096-y · Cited by 707
“power [39], but few have explicitly tested whether ecological responses are statistically significant among ecosystem types.”
BackgroundAn investigation of the false discovery rate and the misinterpretation of p -values
Colquhoun, D. (2014) · Royal Society Open Science · doi:10.1098/rsos.140216 · Cited by 792
Donoho, D. (2017) · Journal of Computational and Graphical Statistics · doi:10.1080/10618600.2017.1384734 · Cited by 695
Climate change perceptions and their individual-level determinants: A cross-European analysis
Poortinga, W., Whitmarsh, L. E., Steg, L., Böhm, G., & Fisher, S. D. (2019) · Global Environmental Change · doi:10.1016/j.gloenvcha.2019.01.007 · Cited by 686
A solution to minimum sample size for regressions
Jenkins, D. G., & Quintana-Ascencio, P. F. (2020) · PLOS ONE · doi:10.1371/journal.pone.0229345 · Cited by 665
“Small N has been discussed as one part of a larger reproducibility problem, where recommended solutions include registered studies, conflict of interest statements, and publication of data [11,13,14,15,16,17].”
BackgroundInfluentialThompson, 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. (2020) · Translational Psychiatry · doi:10.1038/s41398-020-0705-1 · Cited by 629
The Power of Bias in Economics Research
Ioannidis, J. P. A., Stanley, T. D., & Doucouliagos, H. (2017) · The Economic Journal · doi:10.1111/ecoj.12461 · Cited by 634
“Encouraging researchers to combine their data would increase sample size and thereby statistical power (Button et al., 2013).”
BackgroundInfluentialSelf-citationBuilding a Science of Individual Differences from fMRI
Dubois, J., & Adolphs, R. (2016) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2016.03.014 · Cited by 644
“Culprits for the lack of replicability are small sample sizes [138], analytical flexibility [179] which fosters p-hacking [180] without appropriate control for the overall risk of false positives [181], and the bias to publish only statistically significant results (also known as the file drawer problem [182]).”
BackgroundA solution to dependency: using multilevel analysis to accommodate nested data
Aarts, E., Verhage, M., Veenvliet, J. V., Dolan, C., & van der Sluis, S. (2014) · Nature Neuroscience · doi:10.1038/nn.3648 · Cited by 615
“Various recent publications force neuroscientists to acknowledge the possibility that the harvest of their hard labor is contaminated by an abundance of false positive effects 1–4 .”
BackgroundToward a neurology of loneliness
Cacioppo, S., Capitanio, J. P., & Cacioppo, J. T. (2014) · Psychological Bulletin · doi:10.1037/a0037618 · Cited by 623
“The early studies of social rejection were characterized by small sample sizes, which can lead to unreliable effects (Button et al., 2013).”
BackgroundLemoine, N. (2019) · Oikos · doi:10.1111/oik.05985 · Cited by 567
Open science challenges, benefits and tips in early career and beyond
Allen, C., & Mehler, D. M. A. (2019) · PLOS Biology · doi:10.1371/journal.pbio.3000246 · Cited by 608
“…data, and research materials [2,18]; valuing replications and reanalyses [2,5,6,19,20]; changes in statistical approaches with regards to power [21,22] and how evidence is assessed [23]; interactive and more transparent ways of presenting data graphically [24,25]; potentially the use of…”
BackgroundInfluentialBig data approaches to decomposing heterogeneity across the autism spectrum
Lombardo, M. V., Lai, M.-C., & Baron-Cohen, S. (2019) · Molecular Psychiatry · doi:10.1038/s41380-018-0321-0 · Cited by 537
“Such inflated effects present an over-optimistic view on the identified effects and are prone to the winner’s curse [70].”
BackgroundStatistical power for cluster analysis
Dalmaijer, E. S., Nord, C. L., & Astle, D. E. (2022) · BMC Bioinformatics · doi:10.1186/s12859-022-04675-1 · Cited by 710
“As an illustration: the majority of neuroscience studies have a very low power with a median 21% (i.e. only 21 out of 100 true effects would be detected) [28], although it has been suggested this varies notably by sub-discipline [29].”
BackgroundReporting animal research: Explanation and Elaboration for the ARRIVE guidelines 2019Preprint
du Sert, N. P., Ahluwalia, A., Alam, S., Avey, M. T., Baker, M., Browne, W. J., Clark, A., Cuthill, I. C., Dirnagl, U., Emerson, M., et al. (2019) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/703355 · Cited by 557
“Low power has three effects: first, within the experiment, real effects are more likely to be missed; second, where an effect is detected, this will often be an over-estimation of the true effect size [25]; and finally, when low power is combined with publication bias, there is an increase in the false positive rate i…”
BackgroundSassenhagen, J., & Draschkow, D. (2019) · Psychophysiology · doi:10.1111/psyp.13335 · Cited by 549
Midfrontal conflict-related theta-band power reflects neural oscillations that predict behavior
Cohen, M. X., & Donner, T. H. (2013) · Journal of Neurophysiology · doi:10.1152/jn.00479.2013 · Cited by 494
Small Effects: The Indispensable Foundation for a Cumulative Psychological Science
Götz, F. M., Gosling, S. D., & Rentfrow, P. J. (2021) · Perspectives on Psychological Science · doi:10.1177/1745691620984483 · Cited by 610
“…in line with a rising recognition that, in contrast to widespread underpowered studies in psychology, which likely report exaggerated effect sizes (Button et al., 2013; Schäfer & Schwarz, 2019; Szucs & Ioannidis, 2017), effect sizes obtained from well-powered preregistered studies (Funder &…”
Structural brain abnormalities in the common epilepsies assessed in a worldwide ENIGMA study
Whelan, C. D., Altmann, A., Botía, J. A., Jahanshad, N., Hibar, D. P., Absil, J., Alhusaini, S., Alvim, M. K. M., Auvinen, P., Bartolini, E., et al. (2017) · Brain · doi:10.1093/brain/awx341 · Cited by 528
“…distinct epilepsy syndromes share common structural abnormalities There are many sources of inconsistency in previously Second, most cross-sectional brain imaging studies are based on small samples (typically 5 50 cases), limiting the power to detect subtle group differences (Button et al. 2013).”
BackgroundLee, S. L., Pearce, E., Ajnakina, O., Johnson, S., Lewis, G., Mann, F., Pitman, A., Solmi, F., Sommerlad, A., Steptoe, A., et al. (2020) · The Lancet Psychiatry · doi:10.1016/s2215-0366(20)30383-7 · Cited by 572
Inconsistencies and Controversies Surrounding the Amyloid Hypothesis of Alzheimer's Disease
Morris, G. P., Clark, I. A., & Vissel, B. (2014) · Acta Neuropathologica Communications · doi:10.1186/s40478-014-0135-5 · Cited by 522
“Even more so, with recent meta-analyses highlighting some major pitfalls with experimental design and statistical power in neuroscience [208,209], we need to be wary.”
BackgroundGoldberg, S. B., Riordan, K. M., Sun, S., & Davidson, R. J. (2022) · Perspectives on Psychological Science · doi:10.1177/1745691620968771 · Cited by 520
“In addition, similar to recent evaluations of statistical power in neuroscience (Button et al., 2013), we also sought to assess the statistical power of meta-analyses conducted in this area.”
BackgroundTop 10 Replicated Findings From Behavioral Genetics
Plomin, R., DeFries, J. C., Knopik, V. S., & Neiderhiser, J. M. (2016) · Perspectives on Psychological Science · doi:10.1177/1745691615617439 · Cited by 517
“This is especially true in cognitive science (Ioannidis, 2014) and neuroscience (Button et al., 2013), where most research is experimental and sample sizes tend to be small.”
Toward a Science of Computational Ethology
Anderson, D. J., & Perona, P. (2014) · Neuron · doi:10.1016/j.neuron.2014.09.005 · Cited by 524
Do Student Samples Provide an Accurate Estimate of the General Public?
Hanel, P. H. P., & Vione, K. C. (2016) · PLOS ONE · doi:10.1371/journal.pone.0168354 · Cited by 510
Loladze, I. (2014) · eLife · doi:10.7554/elife.02245 · Cited by 430
“Such a small power not only raises the probability of obtaining a false negative to 90–94% but also increases the likelihood that a statistically significant result does not reflect a true effect (Button et al., 2013).”
BackgroundInfluentialSleep, Cognition, and Normal Aging
Scullin, M. K., & Bliwise, D. L. (2015) · Perspectives on Psychological Science · doi:10.1177/1745691614556680 · Cited by 546
“, 2011), which is commonly assumed to decrease statistical power but also increases the risk of Type I errors (Button et al., 2013; Yarkoni, 2009).”
Backgroundvan Rooij, D., Anagnostou, E. A., Arango, C., Auzias, G., Behrmann, M., Busatto, G. F., Calderoni, S., Daly, E., Deruelle, C., Martino, A. D., et al. (2017) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2017.17010100 · Cited by 510
“Last but not least, studies employing small sample sizes are liable to overestimate effect sizes (31).”
Weigand, A., Horn, A., Caballero, R., Cooke, D. L., Stern, A. P., Taylor, S. F., Press, D. Z., Pascual‐Leone, Á., & Fox, M. (2017) · Biological Psychiatry · doi:10.1016/j.biopsych.2017.10.028 · Cited by 492
Male antisocial behaviour in adolescence and beyond
Moffitt, T. E. (2018) · Nature Human Behaviour · doi:10.1038/s41562-018-0309-4 · Cited by 561
Soto, C. J. (2019) · Psychological Science · doi:10.1177/0956797619831612 · Cited by 537
“However, recent metascientific research has raised questions about the replicability of behavioral science (Button et al., 2013; Camerer et al., 2016; Cova et al., 2018; Open Science Collaboration, 2015; Simmons, Nelson, & Simonsohn, 2011; Vul, Harris, Winkielman, & Pashler, 2009).”
BackgroundHoogman, M., Muetzel, R., Guimaraes, J. P., Shumskaya, E., Mennes, M., Zwiers, M. P., Jahanshad, N., Sudre, G., Wolfers, T., Earl, E. A., et al. (2019) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2019.18091033 · Cited by 467
Addressing the theory crisis in psychology
Oberauer, K., & Lewandowsky, S. (2019) · Psychonomic Bulletin & Review · doi:10.3758/s13423-019-01645-2 · Cited by 462
The past, present and future of Registered Reports
Chambers, C. D., & Tzavella, L. (2021) · Nature Human Behaviour · doi:10.1038/s41562-021-01193-7 · Cited by 499
The orbitofrontal cortex: reward, emotion and depression
Rolls, E. T., Cheng, W., & Feng, J. (2020) · Brain Communications · doi:10.1093/braincomms/fcaa196 · Cited by 474
Detecting and avoiding likely false‐positive findings – a practical guide
Forstmeier, W., Wagenmakers, E., & Parker, T. H. (2017) · Biological Reviews · doi:10.1111/brv.12315 · Cited by 436
Publication and other reporting biases in cognitive sciences: detection, prevalence, and prevention
Ioannidis, J. P. A., Munafò, M. R., Fusar-Poli, P., Nosek, B. A., & David, S. P. (2014) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2014.02.010 · Cited by 471
Self-citationHolzman, J. B., & Bridgett, D. J. (2017) · Neuroscience & Biobehavioral Reviews · doi:10.1016/j.neubiorev.2016.12.032 · Cited by 449
Gray, J. P., Müller, V. I., Eickhoff, S. B., & Fox, P. T. (2020) · American Journal of Psychiatry · doi:10.1176/appi.ajp.2019.19050560 · Cited by 449
“Neuroimaging’s promise notwithstanding, recent reports have challenged the reliability of this literature, drawing attention to small sample sizes (5), clinical heterogeneity (6), and flawed correction for multiple comparisons (7), which jointly work to inflate false positive rates.”
BackgroundDeep impact: unintended consequences of journal rank
Brembs, B., Button, K., & Munafò, M. (2013) · Frontiers in Human Neuroscience · doi:10.3389/fnhum.2013.00291 · Cited by 441
“onger, not a weaker correlation. Further supporting the notion that journal rank is a poor predictor of statistical soundness is our own analysis of data on statistical power in neuroscience studies (Button et al., 2013). There was no significant correlation between Consequences of Journal Rank 8 statistical power and…”
BackgroundMethodInfluentialSelf-citationThree ways to make replication mainstream
Gantman, A. P., Gomila, R., Martinez, J. E., Matias, J., Paluck, E., Starck, J. G., Wu, S., & Yaffe, N. (2017) · Behavioral and Brain Sciences · doi:10.1017/s0140525x17001972 · Cited by 302
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
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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.
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