Avoiding Data Pitfalls

Chapter FivePitfall 4: Statistical Slipups

“Facts are stubborn things, but statistics are pliable.”

—Mark Twain

How We Compare Data

In spite of the incredible utility and value of the field of statistics, its name has become somewhat of a byword in our time. Common search phrases that start with “statistics are” include:

• “statistics are bad”
• “statistics are lies”
• “statistics are useless”
• “statistics are not facts”
• “statistics are made up”
• “statistics are for losers”

What gives? Why such disgust for a field that, according to the Merriam-Webster dictionary, is simply “a branch of mathematics dealing with the collection, analysis, interpretation, and presentation of masses of numerical data.”¹ Why is the field of statistics seen in such a negative light by so many?

I believe that the backlash against statistics is due to four primary reasons. The first, and easiest for most people to relate to, is that even the most basic concepts of descriptive and inferential statistics can be difficult to grasp and even harder to explain. Many a befuddled college freshman struggles to get a passing grade in Stats 101 each year. But even many professional scientists struggle to articulate what, exactly, a p-value is.²

The second cause for vitriol is that even well-intentioned experts misapply the tools and techniques of statistics far too often, myself included. Statistical pitfalls are numerous and tough to avoid. When we can't trust the experts to get it right, there's a temptation to ...