Understanding Type I and Type II Errors in Hypothesis Testing

Running head: AAA W2 POST
AAA W2 POST
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AAA W2 POST 1
No test for hypothesis is 100% right. As the test is on the basis of the probabilities,
always there are the chances of making conclusion that is incorrect (Adams & Hester, 2013).
At the time of doing the hypothesis test, two errors can occur: one of them is type I and the
other one is type II.
Type I error:
At the time the null hypothesis is correct and at the same time user rejects that, the
user makes the type I error. α is the probability to make the type I error. An α of 0.05 means
that the user is willing to receive the chance of 5% that the user is wrong for rejecting the null
hypothesis (Sedgwick, 2014). For mitigating the risk, the user have to use a value that is
lower for α. This can be stated that utilizing a value that is lower for the α indicates that the
user shall be less likely to detect a difference that is true if one of them exist really.
Type II error:
At the time the null hypothesis is false and at the user fails to reject that, the user
makes the type II error. β, is the probability to make the type II error that is having the
dependency on the power of testing (McClelland et al. 2015). The risks can be decreased by
the user through ensuring that the test is having enough power. The power of type II error is
(1- β). It is proposed by Cohen (1998), that the probability of maximum error that can be
accepted would be 20% (β=0.2).
Affect:
It can be concluded that if the user reject the null hypothesis that will be a correct
decision and the type I error will occur. On the other side, if the null hypothesis will be failed
to reject by the user, the type II error will occur which is not a correct decision.