Statistical significance or Hypothesis testing? Some seem to insist that Statistical Significance and Hypothesis Testing are different concepts$^\dagger$. Maybe some of them could come forward and explain why they think this way? I came across an interesting article which seems to agree there is no substantial difference.
There might be differences in emphasis, history and culture, maybe, but then what are those, and are they important?
$^\dagger$I got downvoted here so there must be at least one person thinking these concepts are distinct.

Two questions on Cross Validated that contain answers:
What is the difference between "testing of hypothesis" and "test of significance"?
Is the "hybrid" between Fisher and Neyman-Pearson approaches to statistical testing really an "incoherent mishmash"?
Papers that explain in depth with historical context:
Goodman, Toward evidence-based medical statistics. 1: The P value fallacy. https://pubmed.ncbi.nlm.nih.gov/10383371/
Hurlbert, S., & Lombardi, C. (2009). Final collapse of the Neyman-Pearson decision theoretic framework and rise of the neoFisherian. Annales Zoologici Fennici, 46(5), 311–349. (Link to paper)
Lew, M. J. (2012). Bad statistical practice in pharmacology (and other basic biomedical disciplines): you probably don't know P. British Journal of Pharmacology, 166(5), 1559–1567. doi:10.1111/j.1476-5381.2012.01931.x (Link to paper)
A paper that explains the difference and also puts it into the context of scientific inference:
Lew M.J. (2019) A Reckless Guide to P-values. In: Bespalov A., Michel M., Steckler T. (eds) Good Research Practice in Non-Clinical Pharmacology and Biomedicine. Handbook of Experimental Pharmacology, vol 257. Springer, Cham. https://doi.org/10.1007/164_2019_286
