Mill's Inequality is a useful tail bound on Normally distributed random variables.
Mill's Inequality — Let Z ∼ N ( 0 , 1 ) {\displaystyle Z\sim N(0,1)} . Then[1] P ( | Z | > t ) ≤ 2 π exp ( − t 2 / 2 ) t ≤ exp ( − t 2 / 2 ) t {\displaystyle \operatorname {P} (|Z|>t)\leq {\sqrt {\frac {2}{\pi }}}{\frac {\exp(-t^{2}/2)}{t}}\leq {\frac {\exp(-t^{2}/2)}{t}}}
The looser bound shows the exponential shape. Compare this to the Chernoff bound:[2]
P ( | Z | > t ) ≤ 2 exp ( − t 2 / 2 ) {\displaystyle \operatorname {P} (|Z|>t)\leq 2\exp(-t^{2}/2)}