Abstract
Inferential statistics enable researchers to make predictions about a population based on sample data. This involves hypothesis testing where the null hypothesis assumes no effect, and the alternative hypothesis suggests a significant effect. Testing requires assumptions like normality and independence to be validated using tests like Shapiro-Wilk or Levene's for normality and variance. Significant findings are determined by p-values, with values under 0.05 typically indicating non-random effects. Choosing between parametric and non-parametric tests depends on data normality and variance homogeneity. Tools such as t-tests, analysis of variance (ANOVA), and their non-parametric counterparts like Mann-Whitney or Kruskal-Wallis are used based on these criteria, ensuring appropriate conclusions about clinical effects and interventions.