Is Big Data the New Miss Cleo?

Everywhere we look, it seems like companies are scrambling for the crystal ball known as Big Data. Of course, statistics are useless unless you know how to read them. That’s why so many people are clamoring for the best data analysts.

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Everywhere we look, it seems like companies are scrambling for the crystal ball known as Big Data. Of course, statistics are useless unless you know how to read them. That’s why so many people are clamoring for the best data analysts.

In the last few years, data experts have grown in popularity, not just in the office, but also in culture. Nate Silver, the popular former New York Times statistician and writer, wrote an entire book on statistical analysis. And people bought it!

What Silver emphasizes in his book is anybody can read numbers. The hard part is understanding them correctly.

A recent post on Lifehacker examined at some common mistakes people make while dissecting the meanings out of digits. Here are some red flags to keep an eye on.

1. The Base Rate Fallacy: If a company has 75 percent men and 25 percent women, that company is prejudiced, right? Not necessarily, Lifehacker says. Maybe only 10 percent of the applicants the company receives are from women and they are actually being hired more frequently than male applicants.

2. The Extrapolation Mistake. Trends don’t always continue. That seems like a simple idea, but it’s sometimes ignored. Lifehacker uses the example of the smartphone market, saying, “Gartner predicted that by 2012, Symbian would be the top smartphone operating system worldwide, with 39 percent of the market, while Android would have only 14.5 percent.” Obviously, that didn’t happen. Why? The circumstances changed. The Symbian platform wasn’t adopted by smartphone makers as expected, which led to its demise.

3. Correlation Doesn’t Mean Causation. A correlation often suggests a cause, but it’s important to not mistake it as the cause. For example, a Missouri University of Science and Technology study found a correlation between Internet usage and depression. But of course, all people who surf the Internet are not depressed. When a correlation is found between two things, further study is required to find a direct explanation.

What’s the lesson from these common statistical mishaps? You can learn some basic information from a statistical analysis, but often you need to crunch even more numbers to fully understand a complex situation.

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