# Is P value Type I error in hypothesis testing?

I'm confused about the interpretation of P value in hypothesis testing. I know that we set significance level as 0.05 which is the threshold we set for this test so that it won't suffer from Type I error by 5%.

And we are comparing P to significance level, does it mean P is the probability of making type I error based on the sample?

Thanks

The threshold that we set, often written as $$\alpha$$, where we decide that the p-value is sufficiently "weird" to count as evidence against the null hypothesis, is then our probability of a Type I error - it's the probability that, in a universe where the null hypothesis is true, we get data so weird that we think it's false.
A $$P$$-value is the probability that we observe a given result (or something more extreme) assuming that the null hypothesis is true. If we choose to reject the null hypothesis whenever this probability is less than a certain per-determined significance level (say, $$\alpha = 0.05$$), then we will end up rejecting the null hypothesis when it is true for $$5\%$$ of the samples that we take. Hence, the probability of committing a Type I error is the chosen significance level, $$\alpha$$.