<prompt> If we reject null hypothesis, when actually it is true it is known as -Type I errorType II errorPowerSpecificity  <response>: Ans. is a i.e., Type I error Statistical errors o Statistical errors are used to describe possible errors made in statistical decision. o Before reading about the types of error you must know null hypothesis because these tests are related to null hypothesis. o Null hypothesis says -3 Any kind of difference or significance you see in a set of data is due to chance and not significant that means there is no variation difference exists between variables. o Null hypothesis testing e.g., in Chisquare test is used to make a decision about whether : - i The data contradict the null hypothesis --- That means there is true difference which is significant between variables and it is not due to chance. or ii The data approve the null hypothesis --- There is no difference between variables and the difference you see is due to chance. Now see types of error : ? There are two basic type of statistical errors : ? 1 Type I error 2 Type II error Type I error It is also known as an error of first kind or a-error or false positive. o This type of error rejects null hypothesis when it is true False rejection of null hypothesis. o That means in real there is no difference as null hypothesis says but we observe a difference by rejecting the null hypotesis due to error. In very simple words we observe a difference when it is not true false positive. o One of the simplest example of this would be if a test shows that a women is pregnant when in reality she is not, i.e., she is false positive for pregnancy. o Probability of type-I error is given by 13-value probability of declaring a significant difference when actually it is not present. o Significance a level is the maximum tolerable probability of type I error. o Significance a level is fixed in advance and calculation of P value probability of type I error can be less than, equal to or greater than the significance a level. o If the probability of type I error P -value is less than significance a level, the results are declared statistically significant. Therefore, to declare the results statistical significant, type I error a-level should be kept to minimum . o Type I error is more serious that type II error. Type II error It is also known as an error or second kind or error or false negative. o This type of error acceptfail to reject the null hypothesis when it is false --False acceptance of null hypothesis. That means we fail to observe a difference when in truth there is one --4 False negative. o An example of this would be if a test shows that a woman is not pregnant when in reality she is i.e., she is false negative. 