I hate to admit it, but your data is lying to you — and more often than you think. Having clean data with high volume, velocity, and variety doesn’t necessarily protect one from the possibility of reaching faulty conclusions to research questions of interest. Despite what you may have learned in Statistics 101, a significant p-value isn’t always groundbreaking. All data can be coerced and bribed to tell any story; thus, as data practitioners, it’s our duty to be cognizant of the possible pitfalls that abound and how to navigate around common traps — responsibly.

Is more data always better? How can the inclusion or exclusion of data obfuscate a previously held conclusion? In this talk, we’ll address the following paradoxical research questions of interest:

Is an observed event truly a trend? How can previously noted behaviors be a marker for the complete opposite behavior in the future?
Is an association worth my time/money/effort?
When do incontrovertible conclusions lead us not to act on a valid association?