This paper is worth a read.

"Moving to a World Beyond 'p < 0.05' "

https://doi.org/10.1080/00031305.2019.1583913

Excerpt:

There's not much we can say here about the perils of p-values and
significance testing that hasn't been said already for decades
(Ziliak andMcCloskey 2008; Hubbard2016). If you're just arriving to the
debate, here's a sampling of what not to do:

- Don't base your conclusions solely on whether an association or effect was
found to be "statistically significant" (i.e., the pvalue
passed some arbitrary threshold such as p < 0.05).

- Don't believe that an association or effect exists just because it was
statistically significant.

- Don't believe that an association or effect is absent just because it was
not statistically significant.

- Don't believe that your p-value gives the probability that chance alone
produced the observed association or effect or the probability that your
test hypothesis is true.

- Don't conclude anything about scientific or practical importance based on
statistical significance (or lack thereof).

Don't.Don't. Just.don't. Yes, we talk a lot about don'ts. The ASA Statement
on p-Values and Statistical Significance (Wasserstein and Lazar 2016) was
developed primarily because after decades, warnings about the don'ts had
gone mostly unheeded. The statement was about what not to do, because there
is widespread agreement about the don'ts.

The cited works are also worth reading:

Ziliak andMcCloskey 2008
[Paper] https://www.deirdremccloskey.com/docs/jsm.pdf
https://tinyurl.com/y263czrd

[Book preface]
http://www.deirdremccloskey.com/articles/stats/preface_ziliak.php
https://tinyurl.com/y4c8gctx

Hubbard, R. (2016), Corrupt Research: The Case for Reconceptualizing
Empirical Management and Social Science, Thousand Oaks, CA:

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