Wisdom of the crowd is more than the ill-fated television show of the same name. We have all heard the old adage, many minds are better than one, and this can be seen abundantly in nature. Across countless species, nature show us that social creatures, when working together as unified systems, can outperform the vast majority of individual members when solving problems and making decisions. Examples include bees, fish, and ants.
It should come as less of a surprise, then, that humans working together in tandem can efficiently converge decision problems, and even make accurate predictions. This theory of collective intelligence has been studied and analyzed for the past century to try and validate how, when, and in what circumstances it accurately and inaccurately predicts.
Let us go back to a simpler time, when the Astros had yet to win a World Series, the Las Vegas Golden Knights were preparing for the expansion draft, and the hype for The Fate of the Furious was taking over the internet. I am, of course, talking about March 2017.
In my last few posts in this series, I discussed my predictions and conclusions for 2018 MLB end-of-season win totals and playoff odds. However, I decided to step back in this article and examine how this methodology works in greater detail, as well as how it applies to a season where we already know the results, the 2017 season.
Corporations have recently caught on that using sponsorships and influencer marketing techniques within sports products to promote their products is one of the savviest and most reliable ways to reach their target market. It is a great opportunity for them to show consumers their interest in a cause or event and gain their trust, especially in the case of men aged 18-34, a usually hard demographic to reach.
This week, we tackle a common question in today’s realm of Big Data. We all know that there are many systems and methods for collecting data, from web/text scraping, click monitoring, and financial data just to name a few. As a society filled with a rapidly growing amount of data and analytical problems, how do we know when you have enough data? Could there even be situations in which we might have too much data?
Anyone who has taken an introduction to psychology, social sciences, or a statistics class has heard the old adage, “correlation does not imply causation.” This rule posits that just because two trends seem to fluctuate in tandem, this common variance is not enough to prove that they are meaningfully related to one another.
While this sounds nice enough on paper, it is easy to forget when a provocative headline like “Does This Ad Make Me Fat” tricks us into believing a scenario we wish to be true. It would be ideal if we could blame America’s obesity problem on a common annoyance surrounding us everyday, excessive advertising.