 Hello Readers!
Thank you so much for checking in with us. We just wanted to take the time in this short(er) post to update you all on a few changes to the site. First and foremost, we have moved to our own domain! You can now find us at sabersmartblog.com, and all previous links should forward you there. However, hosting and owning our own url and website does have some associated fees. To offset these costs, SaberSmart has now partnered with Amazon.com as an Associate, and the system is remarkably simple:
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 We all are aware that statistics is a tool for converting data into information. Consequently, without data, statistics is all but useless. But where then does data come from and how should it be gathered to ensure its accuracy and reliability? Is it representative of the population from which it was drawn? Today we tackle these issues arising from perhaps the most complicated data source, large populaces, e.g. a vast target population or database. When collecting this kind of data, the two most popular methods are via a census or sampling.
A census is the procedure of systematically acquiring and recording information about the members of a given population. Under this method, data is collected for each and every unit in the population, database, or even universe, for example every person, household, field, shop, factory etc.  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.  Data can provide valuable insights through statistics and other methods of exploratory data analysis. However, there will indubitably come a time when successful communication of these findings become necessary. Graphs and charts provide a great way to communicate data, as well as a method to provide awareness on the stories they can tell. Unfortunately, there are too many incidents of graphs misrepresenting data and consequent conclusions.
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