Inferential Statistics for Hypothesis Testing of Parametrically Distributed Data

In clinical and biomedical research, parametric tests play a crucial role in hypothesis testing when the data follows a normal distribution. This chapter introduces various parametric tests, including the Student’s t-test, paired t-test, and analysis of variance (ANOVA), which are employed to evaluate differences between groups. Essential assumptions such as normality, homogeneity of variances, and independence of samples are discussed, with methods for validating these assumptions provided through graphical (histograms, Q-Q plots) and statistical approaches (Shapiro-Wilk test, Levene’s test). Case scenarios are included to illustrate the application of these tests in real-world clinical trial data. Additionally, step-by-step instructions for performing statistical analyses using R programming are provided, emphasizing both conceptual understanding and practical implementation. t.test function from base package is used to perform independent/ paired t-test whereas rstatix package is used for ANOVA. The chapter equips readers with the necessary tools to select and apply appropriate statistical tests based on study design and data characteristics, fostering accurate and reliable inference in clinical research.