Assignment 29735

Type II error on the other hand is known
as the null hypothesis not being rejected when in fact the hypothesis is true.
Both type I and type II errors should be a concern to researchers because it is
very hard to detect and cannot be avoided. Researchers do have a chance of decreasing
these type of errors if they decided to make their studies with a much more
smaller sample size.

A real life example would be when patients are taking an HIV
test that has an accuracy rate of 99.9%. This implicates that the tests would
not give a false answer. But, as we are aware tests might show a false negative
reading which is why duplicate tests are required. Type I error can be seen in
this case by stating that the null hypothesis is that the patient is not HIV
positive. The hypothesis would actually state that the patient does carry the
virus but due to a type I error it would indicate that the patient has the
virus when they do not. Therefore, this would cause a false rejection of the
null hypothesis. Type II error in this case would indicate that the patient is
free of HIV when they are not. The type II error in this case is more serious
because the null hypothesis has been wrongly rejected causing it to be a
dangerous diagnosis.


When testing a hypothesis there are two possibilities, type
I (having an effect) or type II (having no effect) (Field, 2012).  Type I
error is equivalent to a false positive, and a type II error is a false
negative.  A false positive is when a test is performed and shows an
effect, when there is none. A false negative is the opposite, when a test is
performed and shows no effect, when in fact there is an effect (Andale, 2015).

An example of Type I and Type II error in the real world
would be in a study using pre-employment assessments to accurately predict
hiring suitable and productive employees.  Type I error in the study would
be hiring applicants that are inapt (hiring an applicant you should have
rejected). Type II error would result in the denial of an applicant that may
have been a good fit for the position (rejecting an applicant you should have
hired).  From this hypothetical study, it is clear that type I error can
be more damaging in this type of situation because the employer is hiring an
applicant with potential red flags. Hiring someone that may not have the proper
qualifications, is emotionally unstable, etc. could have a devastating effect
on the company and its employees.  A type II error is more ideal in this
situation because it is a false negative so this selection error usually goes
unnoticed when compared to a type I error. In general, applicants selected from
the type II error pool are less likely to have a negative impact on the
company; however, the employer could miss out on a great hire.