Ask the detail of Neyman-Pearson Lemma (Necessity Part) This is Theorem 8.3.12 (Neyman-Pearson Lemma) in George Casella stat inference. Consider testing $H_0: \theta=\theta_0$ versus $H_1: \theta=\theta_1$, where the pdf pr pmf corresponding to $\theta_i$ is $f(\textbf{x}|\theta_i),i=0,1.$, using a test with rejection region R that satisfies (8.3.1)
\begin{align}
\begin{matrix}
x\in R & \text{if}& f(\textbf{x}|\theta_1)>kf(\textbf{x}|\theta_0)\\
x\in R^c & \text{if} & f(\textbf{x}|\theta_1)<kf(\textbf{x}|\theta_0)
\end{matrix}\tag{8.3.1}\label{8.3.1}
\end{align}
for some $k \geq 0$, and
\begin{align}
\alpha=P_{\theta_0}(\textbf{x}\in R)\tag{8.3.2}\label{8.3.2}
\end{align}
Then (Sufficiency): Any test that satisfies the above is a UMP level $\alpha$ test.
(Necessity): If there exists a test satisfying (8.3.1) and (8.3.2) with k >0, then every UMP level $\alpha$ test is a size $\alpha$ test (satisfies (8.3.2)) and every UMP level $\alpha$ test satisfies (8.3.1) except perhaps on a set A satisfying $P_{\theta_0}(\textbf{x}\in A)=P_{\theta_1}(\textbf{x}\in A)=0.$
I feel difficulty to understand this Necessity. In the proof, it is clear that any test satisfying (8.3.2) is a  size $\alpha$ test. So I don't know why in the Necessity part, we say it again:

If there exists a test satisfying (8.3.1) and (8.3.2) with k >0, then
every UMP level $\alpha$ test is a size $\alpha$ test (satisfies
(8.3.2))

. And the expressions are different. It says we need to satisfy (8.3.1) and (8.3.2). But don't the truth be we only need to satisfy (8.3.2)?
 A: Before trying sketching any arguments, let me introduce some notation and remarks.
Let $T_*(x)$ denote the test described by $(8,3,1)$, that is
\begin{align}
T_*(x)=\left\{\begin{matrix}
1& \text{if}& f(\textbf{x}|\theta_1)>kf(\textbf{x}|\theta_0)\\
\gamma &\text{if} & f(\textbf{x}|\theta_1)=kf(\textbf{x}|\theta_0)\\
0 & \text{if} & f(\textbf{x}|\theta_1)<kf(\textbf{x}|\theta_0)
\end{matrix}
\right.
\end{align}
where $k$ and $\gamma$ is taken so that
$$E_{\theta_0}[T_*(X)]=P_{\theta_0}\big[f(X|\theta_1)>k f(X|\theta_0\big]+\gamma P_{\theta_0}\big[f(X|\theta_1)=k f(X|\theta_0)\big]=\alpha
$$
Notice that The function $t\mapsto P_{\theta_0}[f(X|\theta_1)>t f(X|\theta_0)]$ is positive, monotone nonincreasing and right continuous, so such $k$ and $\gamma$ exists and are unique. If one is dealing with continuous distributions, then the bit $\gamma P_{\theta_0}\big[f(X|\theta_1)=k f(X|\theta_0)\big]$ does not appear.
To show necessity, that is, that any UMP test $T_u(x)$ at level $\alpha$ satisfies $T_u(x)=T_*(x)$ in $\{x:f(x|\theta_1)\neq f(x|\theta_0)\}$, we should understand first what is so special about $T_*(X)$. Once this is established, necessity follows from some basic  measure theoretic arguments that I will only explain towards the end of my answer.

First we reproduce the arguments that show why $T_*(x)$ is a UMP at level $\alpha$, that is, we show that for any other test $T(x)$  with  $E_{\theta_0}[T(X)]\leq\alpha$, we have that $E_{\theta_1}[T_*(X)]\geq E_{\theta_1}[T(X)]$.
Here is more or less how the argument works.
Let $T(x)$ by any other test with power at most $\alpha$, that is $E_{\theta_0}[T(X)]\leq\alpha$. Recall that tests take only values between $0$ and $1$. Notice  that:

*

*If $T_*(x)-T(x)>0$, then $T_*(x)>0$ (for $T(x)\geq0$  for all $x$); hence $f(\textbf{x}|\theta_1)\geq kf(\textbf{x}|\theta_0)$.

*If $T_*(x)-T(x)<0$, then $T_*(x)<1$ (for $T(x)\leq1$ for all $x$); hence $f(\textbf{x}|\theta_1)\leq kf(\textbf{x}|\theta_0)$.

This means that
$$
\big(T_*(x)-T(x)\big)\big(f(x|\theta_1)-kf(x|\theta_0)\big)\geq0
$$
for all $x$. Integration gives
\begin{align}
\int_X\big(T_*(x)-T(x)\big)\big(f(x|\theta_1)-kf(x|\theta_0)\big)\,dx\geq0
\end{align}
simplifying the expression on the left-hand side gives
\begin{align}
E_{\theta_1}[T_*(X)-T(X)]&=\int_X\big(T_*(x)-T(x)\big)f(x|\theta_1)\,dx\\
&\geq
k\int_X (T_*(x)-T(x)\big)f(x|\theta_0)\,dx=kE_{\theta_0}[T_*(X)-T(X)]\\
&=k\big(\alpha-E_{\theta_0}[T(X)]\big)\geq0
\end{align}
Hence
$$ E_{\theta_1}[T_*(X)]\geq E_{\theta_1}[T(X)]$$
In other words, $T_*$ is more powerful that $T$.

Observation: The key parts of the argument above  are contained  (1) and (2).

We are now ready to argue for necessity, that is, that any test $T_u$ that is UMP at level $\alpha$, must be equal to $T_{*}(X)$ in $\{x: f(x|\theta_1)\neq k f(x|\theta_0)\}$.
Suppose now that that $T_u$ is another UMP of power $\alpha$; that is
$E_{\theta_0}[T_u(X)]=\alpha$, and $E_{\theta_1}[T_u(X)]\geq E_{\theta_1}[T(X)]$ for any other feasible test $T$. Then, since $T_*(X)$ is UMP, we must have that $E_{\theta_1}[T_*(X)]=E_{\theta_1}[T_u(X)]$.
Consider the set
$$ A:=\{x:T_*(x)\neq T_u(x)\}\cap\{x:f(x|\theta_1)\neq k f(x|\theta_0)\}$$
The arguments used in (1) and (2) imply that
\begin{align}
\begin{matrix}
\big(T_*(x)-T_{u}(x)\big)\big(f(x|\theta_1)-k f(x|\theta_0)\big)>0&\text{if} &x\in A\\
\big(T_*(x)-T_{u}(x)\big)\big(f(x|\theta_1)-k f(x|\theta_0)\big)=0&\text{if} &x\in X\setminus A
\end{matrix}
\end{align}
Integration gives
\begin{align}
\int_A\big(T_*(x)-T_{u}(x)\big)\big(f(x|\theta_1)-k f(x|\theta_0)\big)\,dx
&=\int_X\big(T_*(x)-T_{u}(x)\big)\big(f(x|\theta_1)-k f(x|\theta_0)\big)\,dx\\
&=E_{\theta_1}[T_*(X)-T_{u}(X)]-k E_{\theta_0}[T_*(X)-T_{u}(X)]\\
&=0
\end{align}
Since $\big(T_*(x)-T_{u}(x)\big)\big(f(x|\theta_1)-k f(x|\theta_0)\big)>0$ for all $x\in A$, then it must be that $A$ is negligible (i.e. $\int_A\,dx=0$). Therefore $P_{\theta_0}(A)=P_{\theta_1}(A)=0$, that is $T_*(X)=T_{u}(X)$ $\{P_{\theta_1},P_{\theta_0}\}$-almost surely.

The last bit is based on a couple of basic measure theory facts:

*

*If $f\geq0$ and $\int f\,d\mu=0$, then $\mu(\{x:f(x)>0\})=0$. That is $f$ must be $0$ almost surely (with respect to the measure $\mu$).

*If $\mu$ is a finite  measure with a density function  respect to another ($\sigma$-finite) measure $\nu$, then $\nu(A)=0$ implies that $\mu(A)=0$. (this is related to a deep result called Radon-Nikodym theorem).

