Abstract
Student’s t test is valid for statistical inference under the normality assumption or asymptotically. By contrast, although the bootstrap t test was proposed in 1993, it is seldom adopted in medical research. We aim to demonstrate that the bootstrap t test outperforms Student’s t test under normality in data. Using random data samples from normal distributions, we evaluated the testing performance, in terms of true-positive rate (TPR) and false-positive rate and diagnostic abilities, in terms of the area under the curve (AUC), of the bootstrap t test and Student’s t test. We explore the AUC of both tests with varying sample size and coefficient of variation. We compare the testing outcomes using the COVID-19 serial interval (SI) data in Shenzhen and Hong Kong, China, for demonstration. With fixed TPR, the bootstrap t test maintained the equivalent accuracy in TPR, but significantly improved the true-negative rate from the Student’s t test. With varying TPR, the diagnostic ability of bootstrap t test outperformed or equivalently performed as Student’s t test in terms of the AUC. The equivalent performances are possible but rarely occur in practice. We find that the bootstrap t test outperforms by successfully detecting the difference in COVID-19 SI, which is defined as the time interval between consecutive transmission generations, due to sex and non-pharmaceutical interventions against the Student’s t test. We demonstrated that the bootstrap t test outperforms Student’s t test, and it is recommended to replace Student’s t test in medical data analysis regardless of sample size.
Keywords: Bootstrap t test; COVID-19; clinical epidemiology; serial interval; statistical hypothesis testing.
【저자키워드】 COVID-19, clinical epidemiology, Serial interval, Bootstrap t test, statistical hypothesis testing., 【초록키워드】 Medical research, Variation, diagnostic, Sex, Intervention, Transmission, outcome, China, clinical epidemiology, Accuracy, Research, Data analysis, Hong Kong, False-positive, Coefficient of variation, AUC, time interval, student, Sample size, statistical inference, Shenzhen, normality, coefficient, T Test, demonstration, assumption, random, Student's t test, bootstrap, statistical, true-positive rate, normal distributions, true-negative rate, defined, performed, significantly, evaluated, occur, demonstrated, adopted, fixed, outperform, statistical hypothesis, TPR, 【제목키워드】 student, bootstrap, example,