Zhou, Y., Lin, S., Zhang, X., Wu, H., Blanchet, J., Suo, Z., & Lu, T. (2024). Is a high-throughput experimental dataset large enough to accurately estimate a statistic? Journal of the Mechanics and Physics of Solids, 183, 105521. https://doi.org/10.1016/j.jmps.2023.105521
Abstract
In materials science, experimental datasets are commonly used to estimate various statistics of random variables. This paper focuses on a specific random variable: the rupture stretch of a material. Examples of statistics include average, standard deviation, coefficient of variation, and different quantiles. How accurate is the estimate of such a statistic? The answer depends on the statistic, the size of the experimental dataset, and how much the random variable scatters. Here we demonstrate a procedure to generate a large experimental dataset and use the experimental dataset to estimate the accuracy of various statistics of the rupture stretch. We use a high-throughput experiment to measure the rupture stretches of 160 specimens of a silicone rubber. We then use the bootstrap method to determine the 90 % confidence intervals of several statistics. We find that the experimental dataset accurately estimates the …
Authors
Yifan Zhou, Sirui Lin, Xuhui Zhang, Hou Wu, Jose Blanchet, Zhigang Suo, Tongqing Lu
Publication date
2024/2/1
Journal
Journal of the Mechanics and Physics of Solids
Volume
183
Pages
105521
Publisher
Pergamon
