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On the unbiased cube, the discrete derivative isFor the p-biased product measure, the normalization factor is $\sigma /2$.

For the indicator $f$ of a monotone family under a p-biased product measure, the Margulis–Russo formula identifies the derivative of ${\mathbb{E}}_{{\mu }_p}f$ with the suitably normalized total influence of $f$.

Write $q=1-p$, $\mu =q-p$, $\sigma =2\sqrt{pq}$, and ${\varphi }_S(x)={\prod }_{j\in S}(x_j-\mu )/\sigma$. The functions ${\varphi }_S$ form the p-biased product measure orthonormal basis, so the Fourier expansion is $f={\sum }_S{\hat{f}}_p(S){\varphi }_S$. The normalized discrete derivative of a Boolean function satisfiesApplying Parseval identity and then exchanging two finite sums gives

The noise operator on the Boolean hypercube acts diagonally on the same basis: $T_{\rho }{\varphi }_S={\rho }^{|S|}{\varphi }_S$. Hence the noise stability isIts derivative isTaking the right-hand value at $\rho =0$ leaves exactly the linear Fourier weight ${\sum }_i{\hat{f}}_p(\{i\}{)}^2$, while taking the left-hand value at $\rho =1$ gives $\mathbf{I}(f)$.

For $m$ tribes of size $w$ with $m\sim (\log 2)2^w$ and $n=mw$, the success probability under the p-biased product measure is $1-(1-p^w{)}^m$. Its $x$-quantile is $p_x=[1-(1-x{)}^{1/m}{]}^{1/w}$. For fixed $0<x<1$,Thus the fixed-error window has order $1/\log n$. For small fixed error the leading coefficient grows with $\log (1/\epsilon )$, so the general Friedgut-Kalai sharp threshold theorem also has the correct error dependence up to absolute constants.