REALITY CHECK Health researchers routinely collect a subset of data (sample) to study disease characteristics. However, the purpose almost always is to generalize findings to the population. Inferential statistics plays a significant role in the generalization of conclusions from sample to population. The inferential statistics uses probability theory to quantify uncertainty for generating evidence in favor or against the intervention. There are several statistical software for data analysis. However, the lack of fundamental statistical concepts may lead to flawed assumptions and incorrect data analysis. The erroneous statistical analysis precedes both inaccurate reporting and interpretation. Thus, it is essential to understand the fundamentals of statistical jargon before delving into the statistical analysis. In this context, the researcher wants to understand few concepts such as: • What is the difference between random selection and random allocation? • What are the differences between standard deviation (SD) and standard error (SE)? • Are α and p value the same or different? • What are type-I and type-II errors?