Here's paper that explains the current objections: https://www.nature.com/articles/d41586-019-00857-9

There is nothing wrong with statistical significance as long as no one is misled by it. Unfortunately, there is plenty of confusion around it and lots of people being misled. Here are two of the concerns mentioned in the article:

1) P > 0.05 is mistakenly taken as evidence of no effect. Tradition is that P > 0.05 should be interpreted as no evidence of anything. For example, run your study with two people and you will find that p > 0.05 for a relationship between exercise and obesity or between teeth brushing and cavities.

2) 0.05 is mistakenly perceived as an indicator of whether things are 100% likely to exist or 0% likely to exist. The challenge here is that the user is not comfortable thinking about probabilities. So a study finding that the difference in cholesterol between Seinfeld fans and Chris Rock fans is statistically significant at 0.0499 is reported as "Seinfeld raises cholesterol."

I think that the central issue here is an objection to having to participate in a research community with people whose training in statistics did not allow them to understand the statistical tools they use in their work.

Then these articles saying scientists are "Against statistical significance" are misleading. It sounds like scientists are more pro statistical significance than ever, so much so that they want it to be defined and interpreted correctly.

I guess many people would benefit from taking a high school statistics class.

Well, in isolation p is problematic. For instance, without hypothesis pre-registration you have the obvious problem with negative results and publication bias.

Some relevant links:

https://www.nature.com/articles/d41586-019-00874-8

https://www.fharrell.com/post/pval-litany/

https://www.scientificamerican.com/article/the-significant-p...