Medical Statistics Series: Inferential Statistics (Part-I)

Abstract

Hypothesis testing (or statistical inference) is one of the most important applications of biostatistics. Most of medical research begins with a research question that can be framed as a hypothesis. There are two type of hypothesis in inferential statistic, Null hypothesis reflects that no difference in comparison to baseline or between groups, whereas an investigator/researcher has some reason to accept difference in comparison to baseline or between groups is known as alternative hypothesis. Since H₀ must be either true or false, there are only two possible correct outcomes in an inferential test; correct rejection of H₀ when it is false, and retaining H₀ when it is true. Therefore, there are two possible errors that can be made which have been termed Type I and Type II errors. A type I error occurs when H₀ is incorrectly rejected. This is commonly termed a false positive. A type II error occurs when H₀ is retained when it is in fact false. This error is commonly termed a false negative. This article explained the statistical hypothesis, type of errors, confidence interval, P- Value and concept of normality.