My apologies, if this topic has been discussed serval times already. In a frequentist approach when fitting a simple linear model on a dataset with sample size say 100 subjects , 6 covariates , each covariate with 3 levels, does p value matter ? How much importance should be given to p values , especially when p > 0.05 in this scenario.

p > 0.05 seldom means very much in any case. But in this case with a very small sample size, if using a frequentist approach I would emphasize confidence limits (compatibility intervals) much more. Note that large p is interpreted as at present we do not have sufficient evidence to say that the data are incompatible with a supposition of no effect, the short version of this being â€śget more dataâ€ť. Bayesian approaches on the other hand provide evidence *in favor* of any assertion.

When the sample size is small, not using any extra-data information is especially problematic, even to the extent that a flat prior that merely disallows the â€śwrongâ€ť direction of a regression coefficient will improve the analysis.

Thanks Frank, this makes perfect sense to me.

One of the truly hard things to do without really a lot of reminders like this is to go with â€śOK, so I still donâ€™t have a good answer, just another question.â€ť Making good use of disappointments is crucial because there are so many more disappointments in data than unexpected treasures.

I had in mind the reaction â€śI didnâ€™t find what I hoped to,â€ť which is the common experience leading to motivated reasoning and dodges such as p-hacking.