Statistical thermodynamics is a theory that connects the microscopic behaviour of atoms and molecules with the macroscopic laws of thermodynamics.
It uses the principles of probability and statistics to predict the average behaviour of a large number of particles, allowing us to understand how the properties of individual atoms and molecules give rise to observable thermodynamic quantities such as temperature, pressure and entropy. Rather than tracking every particle individually, it focuses on the distribution of particles among various energy states and uses this information to compute macroscopic properties to explain the laws of thermodynamics.
Statistical thermodynamics has a wide range of applications in chemistry, including explaining and predicting chemical equilibrium and the transition state theory; deriving expressions for heat capacity, internal energy and entropy based on molecular motions; and understanding phenomena like melting, boiling, and magnetisation by analysing how microscopic interactions change with temperature and pressure.
To fully comprehend the theory, one must first understand the concept of a canonical ensemble.