一般科学家的统计显著性模拟研究
A Statistical Significance Simulation Study for the General Scientist
摘要：
当一个科学家进行实验时，他们通常会获得一组测量结果，并被期望证明他们的结果是“统计学上显著的”，从而证实他们正在测试的任何假设。建立统计学显著性的主要方法包括证明观察到的实验结果是随机机会的产物的概率很低。这通常定义为p < 0.05，这表明观察到的结果随机发生的可能性小于5%。这项研究直观地表明，通常使用的“统计显著性”的定义可能错误地意味着一个重要的发现。这通过生成随机高斯噪声数据并使用基于已建立的双样本t检验的统计检验分析该数据来证明。这项研究表明，在现代科学的许多领域中非常常见的适度大的样本量下，可能会有不显著但“统计学显著”的发现。
英文摘要：
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.
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