Statistical Paradoxes & Logical Fallacies: Don't Believe the Lies your Data Tells
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.
By showcasing enterprise use cases and sample projects in Dataiku's Data Science Studio, Christopher Peter Makris will address the following questions:
- Is more data always better? How can the inclusion or exclusion of data obfuscate a previously held conclusion?
- 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 conclusive conclusions lead us not to act on a valid association?
Presented by Christopher Peter Makris, Lead Data Scientist at Dataiku
Christopher Peter Makris (CPM) is a Lead Data Scientist at Dataiku. With a background in Logic, Discrete Mathematics, & Statistics, he brings experience from both industry and academia. CPM previously stood as Director of Data Science at the NYC Data Science Academy and Executive Director of the Master’s of Statistical Practice Program at Carnegie Mellon University; at both institutions, CPM took pride in professionally developing students, aiding in the redesign of technical curricula, and delivering over 3,000 hours of lectures to graduate audiences. In industry, CPM most recently helped thwart cybercrime as the Director of Security Data Science at Empower Retirement. When not crunching numbers on his computer, CPM is crunching his abs in the studio as he’s an avid dancer, choreographer, and CrossFitter (so, while he considers himself a data scientist, he can only count to 5-6-7-8...)
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