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.

Keywords

• Statistical Inference
• Hypothesis
• Type of Errors
• P-Value
• Concept of Normality