Not sure what is Taguchi method.

First of all, is to check whether the result is consistent with my expectation. Sometimes this step it's good to get feedback from others. For example recently I just finished a script to run some data analysis. The results looked ok to me, but some of my labmates and boss thought some of the numbers were a bit low.

I took a look back at the script and found little bug that takes and process the numbers, where the numbers were being incorrectly reported to the results. So for me, the first step is to take a thorough look at your methods to see if there were any possible errors - that include your reagents.

If the methods and materials seemed fine, then try to analyse why is the results as such. Sometimes unexpected results can be interesting too. This happens very often in phylogenetics studies (study of relationships using genetic data)  - e.g. crocodiles are found to be closely related to birds, the divergence of a species etc.

And the checking of method here is important too. There are so much variance and noise in phylogenetic dataset (missing data, fast-evolving genes, horizontal gene transfers etc) that can contribute noise and in turn, produce misleading assumptions.