Type I & Type II Errors
In statistics, type one error (TIE) occurs when a null hypothesis rejected when it is true. Therefore, it also referred to as a false positive. It used to indicate that a null hypothesis is not likely, or put the difference that is more than the set limits for the likelihood of the null hypothesis (GraphPad, 2014). On the other hand, the difference that falls within the set limits does not result in the probability of the thesis (Gardner & Altman, 2020). This error involves accepting an alternative or real explanation of interest when the result might be due to chance. Therefore, statistician records a difference when there is actually no statistically significant difference. Often, a range of fewer than two errors is taken to mean no difference; however, it is possible to choose three or more standard errors to limit the likelihood of TIE.
In contrast, the type two error (TIIE) occurs when a null hypothesis is not rejected when, in actual sense, the alternative hypothesis remains true. Therefore, the TIIE is also termed as false negative (Lane, n.d.). In this error, the alternative hypothesis is not accepted without adequate power. A non-significant result obtained when comparing two groups does not imply that the two samples were collected from the same population. Instead, it means that there is no proof that they are not from the community (University of Texas, 2011). The error occurs when the statistician does not observe a difference when, in reality, there is a difference. The TIIE happens when the null hypothesis is considered or not rejected, yet there is a difference between groups.