Reply to 2 PSY Research Methods Discussion Posts help



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Type I error is known as rejecting the null hypothesis when in fact it is true (false positive). 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.

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