The Confirmatory Method
IISA Assessment, Consultancy & Research Centre helps you test whether a model is really "significant". This problem is not easily resolved, particularly as it is still unclear which is the best way to draw the inference in context of exploratively data analysis, in which you have to statistically test the entire null hypothesis, or to test alternative hypotheses within the zone boundary or specific understanding locality.
First, tests on the randomness of nature and the origin of the null hypothesis are not entirely useful, besides questioning how strong the power of analysis used to test it. For example, is the null hypothesis that "there is no relationship between motivation and learning achievement" indeed as the way it is? It means, the null hypothesis also needs to be formulated prior to any knowledge of data which must be tested.
Second, if the hypothetical testing is meant only for a review of the "significant", then automatically you have to separate the review of the real significant zone from the fake. Why? Data errors often occur as the very significant culprit since the thing expressed in the alternative hypothesis might have been expected so.
Therefore, if you find a strong enough result to withstand a variety of corrections through some testing, it is very possible that the significance consistency of tests interresult is due to data errors, and not because of particular "real result". How do you cope with this? We are ready to help you get around this difficulty.