Incoherence of $p$-values How do proponents of $p$-values  as measures of evidence (a la Fisher) counter the incoherence arguments of Schervish etc?
In this setting, coherence means that if $A$ implies $B$, the evidence for $B$ should be at least as big as that for $A$.
Do they reject coherence as an essential property of evidence? Do they reject the examples showing $p$ violates coherence?
 A: P-value does not provide evidence for an alternative, but measures evidence against a null hypothesis.
In the Schervish article formulation, he defines the p-value in a way to set up an apparent paradox (creating the one-side p-value vs. two-sided p-value). He cites an example where you would fail to reject $H_0:\mu=0$ because the two-sided p-value is 0.074 but the one-sided p-value for testing $H_0:\mu \le 0$ is 0.037 so you would reject this null hypothesis. This seems to be a contradiction because on the one hand you say you can conclude $\mu$ is not equal to $0$ or any number less than $0$, but in the other case you cannot even say that it is not $0$ using the same data.
It can be made coherent by defining the p-value over a set as the maximum over all the p-values for sets contained in it. Take the specific case of $A=\{0\}$ and $B=(−∞,0)$ with $\hat{\mu}=1.79$ and standard error $S.E.\{\sigma\}=1$. The two-sided p-value for testing $H_0:\mu=0$ would be equal to $2 \times (1-\Phi(1.79))\approx0.074$. The two-sided p-value for testing $H_0:\mu=-2$ would be equal to $2 \times (1-\Phi(1.79-(-2)))\approx0.0001$.  The p-value for B would be $$\max_{\mu_0\in B}\{2 \times (1-\Phi(1.79-\mu_0))\}=0.074$$ This is the approach of only considering two-sided p-values.
Alternatively, it can be made coherent by only considering one-sided tests and one-sided p-values which leads to the "three decision rule" attributed to Neyman. There is a detailed explanation here: Freedman, Laurence S. "An analysis of the controversy over classical one-sided tests." Clinical Trials 5.6 (2008): 635-640.
In almost all common hypothesis testing situations, p-values are:

*

*monotonic. When testing a fixed null hypothesis, what naturally seems like more evidence against the null hypothesis leads to a smaller p-value.

*coherent. For a fixed set of data, when testing two different null hypothesis with the same logic and where one null hypothesis is nested within the other, then the p-values will be in the logical direction expected.
a. For one-sided tests, the p-value for testing $H_0:\mu \le \mu_1$  will be smaller than or equal to the p-value for testing $H_0:\mu \le \mu_2$ if $\mu_1<\mu_2$. That is if the second hypothesis is rejected, then so will the first one.
b. For two-sided tests, suppose there are two points in the null hypothesis $H_0:\mu=\mu_1 \text{ or } \mu=\mu_2$, this is a intersection-union test scenario so the test should be based on the intersection of the rejection regions of the two scenarios $H_0:\mu=\mu_1$ and $H_0:\mu=\mu_2$. That means the p-value for testing this should be the maximum of the two p-values for the simpler null hypotheses. The same idea applies to generalize to the situation of testing $H_0:\mu\le 0$.
Berger, Roger L. "Multiparameter hypothesis testing and acceptance sampling." Technometrics 24.4 (1982): 295-300.

