# Significance in Bayesian statistics

In the context of Mathematics, and more specifically statistics; the term

"significant" has a very specific definition, i.e. where the occurence of a particular event is either attributable to chance (or not as the case maybe)...and in order for an event to be classed as "significant" it must either be typically be equal to or greater than specified value (usually p value of 0.05 or 5%).

Is that correct? My understanding is that the term significant is ONLY used to refer to the fact that an event can be attributed to a particular variable and not mere chance.

I do not mean to sound like I am becoming overly fixated on semantics here, but I assume then that this means that significant and frequency are therefore different, discrete entities? That is to say that whilst something maybe statistically significant, the probability of it actually occurring is very low/remote.

The p value; can be converted to a percentage simply by multiplying the p value by 100.

With that in mind then, does the P value refer to the probability of the event occurring?

The P value is a completely frequentist concept. In particular, a valid P value is a random variable that is uniformly distributed on $[0,1]$ under the null hypothesis. Thus, if in a study we observed that $p\leq 0.05$, it is safe to reject the null hypothesis with type-I error controlled at <5%.