An A/B test run by an e-commerce website. A goal was to help the company understand if they should implement the new page, keep the old page, or perhaps run the experiment longer to make their decision.
None of the variables have significant p-values except for variable 'hour'. Therefore, we will fail to reject the null and conclude that there is not sufficient evidence to suggest that there is an interaction between country and page received that will predict whether a user converts or not.
In the larger picture, based on the available information, we do not have sufficient evidence to suggest that the new page results in more conversions than the old page.
Due to insufficient size of data it is very often responsible for poor performances of learning,and to extract the significant information for inferences is a critical issue. Here in our case, data consists for very short period i.e. for January 2017 only, making it insufficient for variables to predict the users to convert or not. If considering for longer window period range for study, it would make it easy and appropriate for predicting possible outcome for users geting converted or not converted.