Objective: Sample size is one of the crucial and basic steps involved in planning any study. This article aims to study the evolution of sample size across the years from hundreds to thousands to millions and to a trillion in the near future (H-K-M-B-T). It also aims to understand the importance of sampling in the era of big data. Study Design - Primary Outcome measure, Methods, Results, and Interpretation: A sample size which is too small will not be a true representation of the population whereas a large sample size will involve putting more individuals at risk. An optimum sample size needs to be employed to identify statistically significant differences if they exist and obtain scientifically valid results. The design of the study, the primary outcome, sampling method used, dropout rate, effect size, power, level of significance, and standard deviation are some of the multiple factors which affect the sample size. All these factors need to be taken into account while calculating the sample size. Many sources are available for calculating sample size. Discretion needs to be used while choosing the right source. The large volumes of data and the corresponding number of data points being analyzed is redefining many industries including healthcare. The larger the sample size, the more insightful information, identification of rare side effects, lesser margin of error, higher confidence level, and models with more accuracy. Advances in the digital era have ensured that we do not face most of the obstacles faced traditionally with regards to statistical sampling, yet it has its own set of challenges. Hence, considerable efforts and time should be invested in selecting sampling techniques which are appropriate and reducing sampling bias and errors. This will ensure the reliability and reproducibility in the results obtained. Along with a large sample size, the focus should be on getting to know the data better, the sample frame and the context in which it was collected. We need to focus on creation of good quality data and structured systems to capture the sample. Good data quality management makes sure that the data are structured appropriately.