Type 1 error is when a researcher reports that there is a significant difference when there is not, and the Type 2 error is when a researcher reports there is no significant difference when there actually is one. It has been said that the type 1 error is worse than the type 2 error (Thomas, 2012) and I am going to discuss whether that is always the case.
An example, a new drug treating cancer is discovered:
- When committing the type 1 error, the results show the drug is effective when in reality it is not. The drug has many side effects but because it is effective in treating cancer it is given to patients on a regular basis. In consequences, people are not treated against cancer but get more health issues which are the cause of an early death. This shows how serious the type 1 error can be.
- When committing the type 2 error, the results might show that the drug is not effective in treating cancer when in reality it is. Therefore the drug is not administered to people and they die because of the cancer. This shows that committing either of errors might have fatal consequences.
However, consequences of committing any of the errors depend on research. For example, people who get up early have more energy and are more efficient. The significant difference is found even if there is not any. In this case, people’s lifestyle is not going to change and energy levels are not affected. When the type 2 error is committed and the difference is reported even if there is not any, people may start getting early but their energy levels would stay the same. Either way, none of these errors is going to harm anybody.
Researchers try to avoid both types of errors however psychologists tend to be more worried about committing the type 1 error (Howitt and Cramer, 2011) so they reduce the probability of getting the type 1 error but this increases the risk of committing the type 2 error. Researchers to balance the risks of getting one of those errors use the probability rate of 0.05. They also increase the sample size what results in decreasing both, type 1 and 2 errors. According to Lieberman and Cunningham (2009), the real effects should be determined by replication and meta-analysis instead of determining the results from individual studies. They claim that this method makes Type I errors within individual studies less important because they are self-erasing and fewer Type 2 errors occur. Neyman and Pearson (1933) argued that Type 2 error should be controlled in scientific research and Ludbrook and Dudley (1998) argued that Type 1 error should be controlled in biomedical research.
This shows that committing either of errors can have serious consequences or no real consequences at all. Researchers sometimes cannot reduce risk of getting both errors and sometimes they have to decide which one is more serious and would cause more damage. By reducing the risk of committing one of them the risk of committing the other one is increased. Overall, researchers should be flexible and open-minded while designing a research and doing analysis because which error is worse depends on a research.
Statistics without Maths for Psychology, fifth edition.
Introduction to Research Methods in Psychology, third edition.