We un-stacked and reconstructed the decompressed responses. The behavior of the surrogate effects is reflected through the engineered standalone responses. We re-assembled each surrogate-response entry for each of the collected observations by adding together three quantities: 1) the baseline, 2) the partial effect contribution, and 3) the uncertainty which is tagged to the corresponding trial run. We reserved a separate surrogate response for uncertainty by simply retaining the error term around the grand median. The uncertainty surrogate is a mandatory response for checking the uniform stability of the behavior of the error terms across all factor settings. This last action is unavoidable because of the experimental recipes not being replicated. We exchange the inability to locate a single central tendency for the experimental error with an assurance check which could track down any intrusion not spreading evenly across the executed recipes. Otherwise, the effect predictions are bound to be misleading. By decompressing the stacked effects to individual surrogate responses, we isolate the reconstructed datasets such that to be adapted each time for a single-factor comparison treatment with the well-known KruskalWallis test. The Kruskal-Wallis family of reference distributions has been studied extensively in the past. Moreover, the Kruskal-Wallis test has been categorized to complement robust comparison techniques with known power and efficiency properties. Thus, contrasting outcomes have immediate impact not requiring extra calibration or simulation work. Additionally, our technique does not presume that a subgroup of the studied effects should be necessarily weak, thus, it is not limited from the sparsity condition. We bypass the requirement for explicit error variance estimation but still managing to assign statistical significance to each of the studied effects. For the elucidated AP-PCR case, we only needed to contrast separately each of the four surrogate responses at their three respective settings while checking the behavior of the uncertainty response across the four factors for consistency. Discovering statistically significant relationships while engaging the uncertainty response with respect to any of the examined effects could negate the CPI-613 customer reviews decision about the potency of that effect. Such anomalous relationships could occur if specific surrogate response entries receive favorable stochastic ordering notrequiring extra calibration or simulation work. Additionally, our technique does not presume that a subgroup of the studied effects should be necessarily weak, thus, it is not limited from the sparsity condition. We bypass the requirement for explicit error variance estimation but still managing to assign statistical significance to each of the studied effects. For the elucidated AP-PCR case, we only needed to contrast separately each of the four surrogate responses at their three respective settings while checking the behavior of the uncertainty response across the four factors for consistency. Discovering statistically significant relationships while engaging the uncertainty response with respect to any of the examined effects could negate the decision about the potency of that effect.