Hoeffding's Inequality is a statistical theorem that provides a bound on the probability that the sum of random variables deviates from its expected value. Specifically, it applies to independent random variables that are bounded within a specific range. This inequality helps quantify how much the average of these variables can differ from the true mean, offering a way to assess the reliability of sample averages. The significance of Hoeffding's Inequality lies in its application in various fields, including machine learning and statistics. It allows researchers and practitioners to make informed decisions based on limited data, ensuring that predictions remain accurate even with small sample sizes.