Background: This study delves into the intricate relationship between Chebyshev's inequality and the probability of an event, shedding light on how mathematics provides insights into the likelihood of events across various distributions. Methods: In this study, we start by providing a comprehensive overview of Chebyshev's inequality, elucidating its formulation and inherent characteristics. We then delve into the theoretical foundation of event probability, presenting the mathematical framework that quantifies the likelihood of an event occurring within a given dataset. By combining elaborating on these two concepts, we develop a novel approach that provide us with the exact probability of an event. Results: Our investigation reveals that Chebyshev's inequality provides only an upper limit on the probability of an event occurring, contingent on the number of standard deviations from the mean within which the event resides. As the number of standard deviations increases, the bound on the event probability tightens, presenting a conservative estimate that holds true for diverse datasets. This result is particularly significant in scenarios where the underlying distribution lacks well-defined characteristics or is not normally distributed. Conclusions: In conclusion, this study illuminates the dynamic relationship between variance and the probability of an event.

\href{https://doi.org/10.5281/zenodo.8312893}{Zenodo Version 1}