P-value is the probability of ?

Ans. is 'b' i.e. Rejecting a null hypothesis when true Here I am explaining the p-value in relation to statistical error. P-value and Type I error Type I error (alpha error) occur when the null hypothesis is true but is rejected erroneously, which leads to an erroneous conclusion that a difference exists between the two samples. If a test has statistic with a p-value of less than 0.05, it is accepted as statistically significant and the null hypothesis is rejected. In this situation, approximately 5% of the time type I error is made because if no difference actually exists between groups, that result will occur by chance in 5% (1 in 20 times). How is it possible ? It has already been explained that p value is 0.5 means that there is 5% chance of null hypothesis being true and 95% chance of being false; and if a study results have p value of S 0.5 the null hypothesis is rejected and the difference between groups is considered as statistically significant. So, there are 5% chances that null hypothesis is true but rejected --> Type I error. So, p-value tells about the probability of type I error (i.e., rejection of null hypothesis when it is true) If, p-value is 5 0.001 a type 1 error will occur 0.1% of the time (1 in 1000 times) Therefore, chance of making a type I error is decreased if you use a lower p value. So, If one rejects the null hypothesis, one risks committing a type 1 error, the probability of which is equal to the p value.