Statistical hypothesis testing: general approach in medical research

Abstract

Statistical hypothesis testing is one of the key steps in modern medical research. Initially, scientists formulate a research hypothesis based on which the statistical hypothesis is then developed and statistically tested. This review provides the null and alternative hypotheses’ compiling examples for different research questions and the general algorithm for their testing using t-test. The authors also describe type I errors, which are necessary to interpret p-values estimated from statistical tests, and type II errors, which are used to assess study power. The article focuses on effect size and its calculation methods, and the difference between statistically significant and clinically significant effects. The associations between effect size, sample size, and type II error are also discussed.

Keywords

• statistics as topic
• null hypothesis
• type i error
• type ii error
• effect size
• t-test