Answer:
a.
Explanation:
The p-value is a measurement of the likelihood that a difference observed is due to a random chance or a sampling error. In an alternative way, the p-value of a study represents the probability or area under distribution for obtaining more radical outcomes whenever the null hypothesis is true.
Any observable change is deemed to be addressed by sampling variability if the P-value is greater than the selected alpha level. A statistical test will nearly always show a substantial difference with a suitably big sample unless there is no impact at all when the effect size is exactly zero.
As a result, simply reporting the P-value alone for a study is insufficient to fully validate the results and findings of scientific publications.