The article "Power failure: why small sample size undermines the reliability of neuroscience" by Button et al. highlights the significant issue of low statistical power in neuroscience research, which reduces the likelihood of detecting true effects and increases the risk of false positives. The authors argue that the average statistical power of studies in neuroscience is very low, typically between 8% and 31%, due to small sample sizes, small effect sizes, and other factors. This low power leads to overestimates of effect sizes and low reproducibility of results. The problem is exacerbated by biases such as publication bias, selective reporting, and the winner's curse, where small studies are more likely to report inflated effect sizes. The article provides empirical evidence from various subfields of neuroscience, including neuroimaging and animal model studies, to support these claims. It also discusses the ethical implications of low power, including inefficiency and waste in research. The authors recommend several strategies to improve reproducibility, such as performing a priori power calculations, transparent reporting, pre-registering study protocols, making data and materials available, and incentivizing replication studies.The article "Power failure: why small sample size undermines the reliability of neuroscience" by Button et al. highlights the significant issue of low statistical power in neuroscience research, which reduces the likelihood of detecting true effects and increases the risk of false positives. The authors argue that the average statistical power of studies in neuroscience is very low, typically between 8% and 31%, due to small sample sizes, small effect sizes, and other factors. This low power leads to overestimates of effect sizes and low reproducibility of results. The problem is exacerbated by biases such as publication bias, selective reporting, and the winner's curse, where small studies are more likely to report inflated effect sizes. The article provides empirical evidence from various subfields of neuroscience, including neuroimaging and animal model studies, to support these claims. It also discusses the ethical implications of low power, including inefficiency and waste in research. The authors recommend several strategies to improve reproducibility, such as performing a priori power calculations, transparent reporting, pre-registering study protocols, making data and materials available, and incentivizing replication studies.