Sample size, a fundamental concept in statistical analysis, refers to the number of observations or individuals included in a study or experiment and is crucial for ensuring the reliability and accuracy of results. Larger sample sizes generally lead to more reliable outcomes, akin to casting a wider net for a more accurate understanding of a population. Factors determining sample size include total population size, desired precision, anticipated effect size, statistical power, and real-world constraints such as participant availability. For data scientists, especially in software companies, balancing these factors is key to designing experiments that yield actionable insights. When resources are limited, careful planning is necessary to maximize the potential of available data. Understanding the importance of sample size and its impact on statistical power is essential in creating effective experiments and analyses.