Type I and Type II Errors

Type I and Type II Errors

Type I error is alternatively referred to as false positive and occurs when a researcher incorrectly disregards a true null hypothesis. In other words, Type I error occurs when a null hypothesis is actually true, but research findings or testing proves and terms it as false. This means that a researcher can perform a Type I error when he/she finds that the report’s findings are significant, but in reality, the results have occurred by chance (McLeod 2019). An example of this occurrence is when an individual has a null hypothesis that wearing their lucky socks makes a difference in their game wins. Then they decide to test their hypothesis by alternating the” lucky socks” and” non-lucky socks.” While they are not wearing the socks, they win 45% of the games, and with the socks, they win 55% of the games. Since the p-value is small enough, then the conclusion is that the lucky socks make a difference. Therefore, if the truth is that the socks have no positive or negative impact, then the conclusion is wrong, and Type I error has occurred.

Type II error, also referred to as false negatives, occurs when a null hypothesis is true but is regarded as false by testing or research findings. The existence of a significant difference is disregarded. An example is when an individual has a theory which forms a null hypothesis that a certain shampoo does not make hair grow faster than the normal shampoos. The hypothesis is then tested, and the p-value is not small enough to reject the hypothesis (Strand & Jaynes 2019). Suppose the null hypothesis is actually true, and the shampoo does make growth difference, but the testing results direct the individual not to reject the null hypothesis. In that case, a Type II error occurs.

References

McLeod, S. (2019). What are Type I and type II errors.

Strand, M., & Jaynes pHD, J. (2019). Accounting for Type 2 Error in the Judgment of                  Significance of Effects in a Two-level Factorial Design.