Therefore, the proposed method possesses internal error-checking properties which decode the strength of the perturbing uncertainty. In lack of replicated data, the proposed technique undertakes a search for clues from recipe to recipe in order to spot inconsistencies and extremities with respect to the size of the uncertainty. The new approach encourages the efficient use of the information content in each single observation appreciating the aspect that unreplicated OA datasets are scarce and thus precious resources for knowledge discovery. Direct competing non-linear techniques that incorporate screening and optimization in a single step for saturatedunreplicated OA schemes are still in the developmental stage. To realize the usefulness of the proposed technique, we tabulated the corresponding ANOVA and GLM outputs in Tables 4 and 5, respectively. We observe that in both treatments the data processing has been aborted hastily due to the disappearing of any remaining degrees of freedom that could be associated to the experimental error. The only qualitative information we may extract from Table 4, for instance, it might be that the MgCl2 and the primer concentrations should lead the strength hierarchy in the examined group of effects. But the statistical importance of such an outcome cannot be quantified. Freeing up some degrees of freedom which have been previously awarded to the effects may permit the statistical estimation of the experimental uncertainty. This tactic was suggested through the error-pooling approach found in the standard Taguchi-methods toolbox. Nevertheless, a convenient error-pooling maneuver Perifosine merely seeks to dislodge the weakest performing effect while disguising it instead as an entrapped quantity posing as the residual error. This is usually accomplished by identifying first and then removing the weakest effect from the initial list of the contrasted factors in the ANOVA treatment. The isolated variance of the weakest effect then enters the F-test comparison step in ANOVA playing the role of the unexplainable error. Thus, this trick enables ANOVA to return estimations of statistical significance for the rest of the examined effects in the group by lifting the roadblock of the indeterminate uncertainty in connection with the depleted degrees of freedom. Generating ANOVA results in this fashion is still viewed as greatly subjective because the unexplainable error is rendered: 1) quantized and 2) framed to the size of the disturbance caused by the weakest effect. Therefore, it becomes debatable whether the contribution of the uncertainty should be allowed to be limited to absorb only the weakest effect. Thus, the decision still looms with regards to what extent would be justifiable for other weaker effects to join in forming the residual error term. Similar discussion follows from using GLM regression to quantify the dominant effects. Alternatively, the non-linear gauging of the effects may be approximated by dichotomizing each contrast first in linear and quadratic components in order to set them up appropriately for treating them with the Lenth test. However, in such case the effects are diluted before they are fed to the data analyzer.