“Figures may not lie, but liars figure”


If you’ve taken a statistics class, you may have heard jokes like, “80% of all statistics quoted to prove a point are made up on the spot.” We laugh at this because it’s ironic, but unfortunately, there’s truth to it: statistics and figures, while maybe not as blatantly false as this joke implies, are neither bulletproof nor completely objective and can easily be distorted or misapplied to prove something that’s far from accurate.

    Though statistics is a relatively modern field, it’s become valuable in many fields because of its perceived ability to produce numerical, objective information. While it’s true that these figures are likely objective in and of themselves, what goes into determining them is often subjective. A statistic on the number of homeless persons in a state may seem straightforward until you ask what constitutes as homeless: Is someone considered homeless if they don’t own a house and are crashing on their buddy’s couch most of the week? If so, does that mean that living with your parents or cohabiting makes you homeless? What about the retired couple who travels the country in an RV?

    Depending on how you answered these questions, you might have a very different (might I even say subjective?) definition of homelessness than the person in the stall next to you (or wherever you may be reading this). It’s easy to see how two researchers could report wildly different figures for the same issue. Assuming the researchers followed proper procedure, neither set of figures is necessarily wrong.

    This isn’t to say all figures are accurate or equal in value, however. Some research is deliberately twisted to support a business or cause. Sometimes sample sizes are too small or aren’t selected randomly. Even if original findings are accurate, they may later be tweaked, misrepresented, or outright changed.

    These occurrences, while disappointing, don’t surprise me. Scientists are only human, after all. What worries me most is that we accept so many statistics merely because they seem factual, black-and-white and objective. This is not the case, as we’ve seen. Yet mass media continues to thrive off of reporting results of the newest studies, as we continue to mindlessly devour their statistics. We mentally note them for our next argument, insert them throughout our papers to validate our work, and allow them to influence our views.

    This is wrong. As intelligent people looking for the truth, we should be questioning all figures, cross-examining them, searching for bias in them, and looking into the methods the researcher used to extract them. Of course, this may be difficult for us students because of lack of experience, but there’s no reason we can’t try or at least bring figures to the attention of those more experienced.

    To professors who are generating statistics of your own and/or using statistics to teach us: please, please remember to be critical and skeptical of all figures, no matter whether they support your opinions and beliefs, and encourage us to do likewise. Statistics are obviously incredibly influential, but to allow us to blindly accept them at face value is to risk producing ignorance, obtuseness and nescience- certainly not qualities we expect from the “light and splendor of the Republic.”