![]() Since β is the probability of a Type II error, the power of the test is defined as 1- β. Power of Test: The Power of the test is defined as the probability of rejecting the null hypothesis when the null hypothesis is false. Type I error is false positive while Type II error is a false negative. Two correct decisions are possible: not rejecting the null hypothesis when the null hypothesis is true and rejecting the null hypothesis when the null hypothesis is false.Ĭonversely, two incorrect decisions are also possible: Rejecting the null hypothesis when the null hypothesis is true(Type I error), and not rejecting the null hypothesis when the null hypothesis is false (Type II error). The table given below explains the situation around the Type I error and Type II error: Decision The probability of a Type I error is denoted by α and the probability of Type II error is denoted by β.įor a given sample n, a decrease in α will increase β and vice versa. On the other hand, a Type II error is made when we do not reject the null hypothesis and the null hypothesis is actually false. When we perform hypothesis testing we consider two types of Error, Type I error and Type II error, sometimes we reject the null hypothesis when we should not or choose not to reject the null hypothesis when we should.Ī Type I Error is committed when we reject the null hypothesis when the null hypothesis is actually true. What is the difference between Type I Error & Type II Error? Also, Explain the Power of the test? Data Science Interview Questions for Freshers 1. If you are aspiring to be a data scientist then you can take up data scientist courses, refer to the below interview questions and crack the interview. Here we will provide you with a list of important data science interview questions for freshers as well as experienced candidates that one could face during job interviews. Interviewers seek practical knowledge on the data science basics and its industry-applications along with a good knowledge of tools and processes. ![]()
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