According to Huck (2014), chapter twelve, the problem of compounding the Type 1 error is that it yields inconsistencies and ambiguities that eventually affect the interpretation of the data obtained. This raises problems concerning the control of the Type 1 error rates that…
In most cases, the effects of multiple tests are ignored and their interaction in the two-way ANOVA.
According to Huck (2014), chapter eleven, some of the recommended solutions to compounding the Type 1 error is through ignoring them or by the use of ANOVA. The ANOVA works by comparing the variance within each sample population and the variance between different samples. The variance between and within the samples are computed by getting the sum of the squares then using different formulas to obtain the final result. First, compute the variance between the samples then compute the variance within the samples. Next, computation of the ratio that exists between the variance obtained from between and within to obtain the F ratio.
If the null hypothesis is true it implies that the variance between the samples should be equal to zero. Is the converse is the case, the F ratio is larger and the bigger the value gets, the more the chances of rejecting the null hypothesis. Another solution is through the Bonferroni adjustment in which case the alpha is divided by the number of tests. This helps in minimizing on the effects of factorial issue on the final result (Ware & Brewer, ...
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