“Can’t-Fool-Me” Culture, The Ostrich Effect, and Systemic Racism

Based on modern behaviour, getting duped seems to be one of the worst things that can happen to a person.

There’s a perverse pleasure in calling out a “fake” social media post. (And there’s great shame in not seeing the sham.)

The spread of conspiracy theories attests to the pride we receive when “debunking” mainstream opinions.

And the dramatic rise of Atheism over the past century demonstrates our shift towards skepticism.

There’s really nothing particularly wrong with any of that. The growing demand for empirical evidence and the rise of critical thinking is healthy; it’s way harder to be a charlatan nowadays.

But there’s a danger here. As we set a high burden of proof, we adopt the stance: “I’ll believe it when the facts come in.” Put another way: “Until I see proof, it doesn’t exist.”

Enter: The Ostrich Effect.

We’re all susceptible. The ostrich effect is self-serving (albeit only mentally). We avoid opening mail because we don’t want to see our bills. We don’t go to the doctor because we don’t want a diagnosis.

We aren’t forced to disrupt our lives as long as we hold out for compelling and unignorable evidence. But what evidence are we waiting on? The answer is obvious when it comes to our bills and diagnosis — but what about systemic racism? What data would prove without a doubt that this is a present and pressing issue?

I used to endlessly listen to podcasts from guys like Sam Harris, who I saw as “ultra-rational.” They rely solely on empirical evidence to shape their belief in what is real. Their favourite phrase is, “well actually…” and I loved parroting their arguments against popular beliefs. It made me feel smart. As I look back though, I’m wondering if I wasn’t simply drinking a false-logic Kool-Aid.

I’ll always remember this quote of Sam’s from a podcast about police brutality and race in 2016: “Stats are the only way you can aggregate behaviour. You can’t just show six videos and tell your personal story and think you have a handle on this.” When I heard this, I nodded along. Anecdotal evidence on the prevalence of racism in police conduct didn’t hold up to the broader picture the statistics painted: police brutality is a serious issue but it’s race-ambiguous; and while racism surely exists, it’s rare.

That’s the trap: statistics are the sand we bury our heads in to pretend there’s nothing to see. Funny how the stats Sam (et al) parade around are always self-serving. We’d rather not confront how privileged we are and how rampantly racism exists, so let’s find some stats to diminish the issue.

I heard another familiar quote from Ben Shapiro recently: “Stats are not racist. Stats are stats.” Sure, Ben, but how you apply those stats can sure a shit be racist.

Maybe your stat says: “black men — on the whole — disproportionately commit more crime in America than white men,” so you believe it’s logical for police to be more suspicious of a black man jogging through a neighbourhood. He’s “more likely” to have committed a crime than a white man, right? Racial profiling makes sense, right?

But is that what your stat actually shows — that we should be more wary of a black man than a white man in Georgia? Is that how we should be applying the insights you gleaned? Does your stat account for geography? Or is your data skewed by pockets of highly-concentration crimes being committed in low-income areas — and bear no real relevance outside of these areas?

When you aggregate behaviour through your stats, how accurate of a picture are you really getting? Are you using stats to supplement your understanding of the world? Or are you using them as a smokescreen to pretend all the anecdotal evidence are a series of one-off instances? Are you hiding from an uncomfortable reality while assuming a form of rational superiority?

We enjoy being hard to fool. What we don’t realize is there’s a thin line between skepticism and denial, and we’re guilty of the latter far too often.

Be careful as you listen to statistics. Though easy to hide behind, data rarely tells the whole story, and it’s easily manipulated to paint the picture we want to see. Weigh the stats against the observed reality. Where they clash, ask why. Are we really focusing too intently on narrow issues? Or is the data being paraded around failing to capture what’s happening?