All sides are not the same. First, science is a process with the goal of objectively determining truths about the world. Already it is wildly different from most other processes whose goals are things like building something, making money, influencing people, etc. Just that different alone is huge. It's like comparing runners to people who don't even enter the race and claiming they are the same.

But what most people refer to when they talk about science is the results of the process. There too, we have a wide spectrum of quality. It's rather dishonest to throw all results into a single bucket and then cherry pick the poor quality results and claim everything is the same. It is not.

On p-values, yes, there is currently a large problem with some areas of science not understanding statistics correctly. This is not a problem with the theoretical process or goal, it is a problem with implementation that should be addressed. But again, to compare even this with non-scientific information which is literally made up to suite various non-scientific goals is very dishonest. These bodies of information are not remotely "the same."

The whole post-truth this article refers to is more about the wider dissemination of scientific knowledge to non-practitioners. While it's always been a challenge to communicate sometimes complex information to the wider public, what has changed in society today is arguably the amount of "corrupted" versions of the data. When you have people taking the scientific results and producing slightly distorted versions with the intention of misleading or making it hard to discern what is original, it's a bigger problem. This is what the article is referring to as "post-truth."

It's a wider societal problem that affects both scientific knowledge and other information alike. Let's not dismissing the problem by saying "all sides are the same." There is a very large range of quality of information, it is not all the same. The amount of bias and ulterior agenda in information varies hugely.