Why are probabilities distribution of thermodynamic variables tend to Gaussian?

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The probability distribution for a thermodynamic variable x is expressed as P = N e^{-A(x)/KT}, where A(x) can be represented by different free energy functions depending on conditions. The Taylor series expansion of A(x) around equilibrium shows that the first derivative is zero at equilibrium, allowing for truncation of higher-order terms. This truncation leads to a Gaussian distribution for P, centered at x0 with a standard deviation dependent on the second derivative of A at equilibrium. The justification for this truncation relies on the assumption that |x - x0| remains small, particularly when the number of particles N is large, ensuring fluctuations around equilibrium are minimal. Understanding this relationship is crucial for applying statistical mechanics in thermodynamics.
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The probability distribution for some thermodynamic variable x is given by

P = N e^{-A(x)/KT}

where A(x) is the availability, which can be replaced by Hemlholtz free energy F, Gibb's free energy G, etc depending on the conditions imposed. N is just some normalization constant. A(x) can be expanded in a taylor series about the equilibrium conditions,

A(x) = A(x_{0}) + (x - x_{0})(\frac {\partial A} {\partial x})_{x = x_{0}} + \frac{1} {2} (x - x_{0})^{2} (\frac {\partial^2 A} {\partial x^2})_{x = x_{0}} + ...

The second term is 0 since dA/dx = 0 at equilibrium. If we truncate all the other terms, clearly we see that P will be a Gaussian distribution with mean of x_{0} and standard deviation of

\sqrt {\frac {K T} {(\frac {\partial^2 A} {\partial x^2})_{x = x_{0}}}}

What is the justification for truncating this series? This is justified if (x - x0) is small. But why will it be small for big N?
 
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I am not familiar with the details of the physics. However such truncation would be based on the assumption |x-x0| is small.
 
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