There are many questions and answers on how to apply p-value adjustments and in which cases, but I couldn't get an answer to my problem based on those, so here goes :
Assume you have a dataset with 5 quantitative variables and 2 qualitative variable (let's call them vquants and vquals for quick reference). You want to see if vqual-1 has an effect on any of your vquants, so you repeat something like an anova to check for each vquant. I understand this particular process alone could call for a p-value adjustment (such as Bonferroni for example).
Further in your analysis, you want to do the same thing for vqual-2.
Are the two sets of 5 anovas to be considered as different processes (and apply a $\alpha/5$ correction to both separately) or do you have to account for both sets as one big test repetition scenario (thus applying a $\alpha/10$ correction to all test)?