Great list of ways to refer to “close to, but not really, significant results.” Given how much P values can jump around using samples from the same population,my suggestion is to give the actual P value and talk about the effect size. No need to describe the P value itself.
What to do if your p-value is just over the arbitrary threshold for ‘significance’ of p=0.05?
You don’t need to play the significance testing game – there are better methods, like quoting the effect size with a confidence interval – but if you do, the rules are simple: the result is either significant or it isn’t.
So if your p-value remains stubbornly higher than 0.05, you should call it ‘non-significant’ and write it up as such. The problem for many authors is that this just isn’t the answer they were looking for: publishing so-called ‘negative results’ is harder than ‘positive results’.
The solution is to apply the time-honoured tactic of circumlocution to disguise the non-significant result as something more interesting. The following list is culled from peer-reviewed journal articles in which (a) the authors set themselves the threshold of 0.05 for significance, (b) failed to achieve that threshold value for…
View original post 2,779 more words
Pingback: What’s going on with p values? – Stats Come 2 U