标题： 对普通科学家的统计显著性模拟研究 显示英文标题

标题： A Statistical Significance Simulation Study for the General Scientist

摘要： 当科学家进行实验时，他们通常会获得一组测量数据，并被期望证明他们的结果是“统计显著的”，从而确认他们所测试的假设。 建立统计显著性的主要方法是证明观察到的实验结果由随机偶然因素产生的概率较低。 这通常定义为 p < 0.05，这意味着观察到的结果随机出现的可能性小于5%。 这项研究通过可视化展示，表明常用的“统计显著性”定义可能会错误地暗示一个显著的结果。 这是通过生成随机高斯噪声数据并使用基于已建立的两样本t检验的统计检验来分析这些数据来证明的。 这项研究表明，在中等偏大的样本量下，不显著但“统计显著”的结果在现代科学的许多领域中是非常常见的。 显示英文摘要

摘要： When a scientist performs an experiment they normally acquire a set of measurements and are expected to demonstrate that their results are "statistically significant" thus confirming whatever hypothesis they are testing. The main method for establishing statistical significance involves demonstrating that there is a low probability that the observed experimental results were the product of random chance. This is typically defined as p < 0.05, which indicates there is less than a 5% chance that the observed results occurred randomly. This research study visually demonstrates that the commonly used definition for "statistical significance" can erroneously imply a significant finding. This is demonstrated by generating random Gaussian noise data and analyzing that data using statistical testing based on the established two-sample t-test. This study demonstrates that insignificant yet "statistically significant" findings are possible at moderately large sample sizes which are very common in many fields of modern science.