The danger is that any set of numbers can be fed into a statistical program package to produce a P value. The proper procedure is to State the hypothesis to be tested. Collect relevant data. Select a test procedure and obtain a P value, which is a measure of whether the data are consistent with the hypothesis. It is not the whole answer.
Statistical significance is irrelevant if the effect is of no practical importance.
That said, significance tests are an important and useful piece of the puzzle. Every so often, a cry is raised that P values should no longer be used because of the way they can be abused. Those who would abandon significance tests entirely because of the potential of misuse make an even greater mistake that those who abuse them. The difference between a good analyst and a bad analyst is that the bad analyst follows each step without any idea of what is necessary to insure the validity and generalizability of the results. The bad analyst sees only P 0.05 with no regard for confidence intervals or for the context in which the data were collected.