Statistical Models: Fermi-Dirac & Beyond - Key Features & Usage

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Fermi-Dirac and Bose-Einstein statistics are the two principal statistical models used to describe particles at the atomic level. Fermi-Dirac statistics apply to particles with half-integer spin, such as electrons, and adhere to the Pauli exclusion principle, meaning no two particles can occupy the same quantum state. In contrast, Bose-Einstein statistics apply to particles with integer spin, allowing multiple particles to occupy the same state, which is crucial for phenomena like Bose-Einstein condensates. Understanding the characteristics and applications of these models is essential for selecting the appropriate one for a given physical system. The derivation of these models stems from quantum mechanics principles, linking their usage to the nature of the particles involved.
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I understand that there are a couple of statistical models out there that describe physical systems. One I know is Fermi-Dirac statistics. What are the other models, what are their key features and when are they applied? When working with a system, how can you be sure you should be using this particular model.

Any clues on how these models "derived" if they were at all. Thanks for your input ^^
 
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The two principal statistics describing particles at the atomic level are Fermi-Dirac and Bose-Einstein. The F-D describe particles with half integer spin (electrons, protons, neutrons,etc.), while B-E describe particles of integer spin (H1 atoms, photons, etc.). One major (maybe the most important) difference between them is that F-D particles obey the Pauli exclusion principle, i.e. only one particle may be in a given state (the standard description of electrons in atoms results from this), while B-E particles do not (leading to experiments involving B-E condensates - you can look it up).
 
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