I’m Sorry, but Your Friend Is Wrong

identifying and addressing Hidden biases

You’ve heard it before, probably even this week: “Everyone’s talking about it.” “People are upset about this.” “The internet is up in arms about that.” But when you actually read the article or press for details, the “proof” boils down to a handful of viral tweets, a pundit’s opinion, or something someone said on a podcast.

As a researcher, this drives me nuts. We are living through a time when real journalism is rarely the loudest voice in the room – most of the daily news is driven by one-off anecdotes where the headlines carry more weight than actual data. 

And the worst part? This isn’t just affecting media and pop culture. People in business are also building products and strategies on shaky ground and flashy trends. It’s time to call it what it is: biased thinking masquerading as insight.

We are constantly bombarded with information on a daily basis, and it can be hard to sift through what is true and what is misinformation. I’m going to break down two of the biggest offenders with the hopes that it will help you better navigate choppy data waters: recency bias and anecdotal evidence. 

Recency Bias: Objects In Mirror Seem Larger As They Appear

Recency bias is our brain’s tendency to give greater importance to events that happened closer to the present, even when older data is more representative or useful. In short: the last thing we saw feels like the most important thing, even when it’s not.

This is especially dangerous in industries that depend on long-term analysis – like finance, public policy, or healthcare. This year, one of the biggest victims of recency bias has been air travel. Stories about irregular landings, plane crashes, and other aviation accidents seem to be popping up left and right. It has been a trending series of stories in the news and has caused consumer trust in air travel to drop on the whole. 

But according to recent reporting from CNN, commercial air travel is statistically just as safe (if not safer) than it has been in decades. There is no trend line supporting an increase in risk. Just noise. But because those incidents happened recently, people now bring that fear into their travel decisions.

The same mistake often happens in business. A single quarter of underperformance suddenly “proves” a product is dying and needs a dramatic change. The stock market hits a big surge after several down weeks and now “the market is back.” One viral campaign flops and a brand changes its whole strategy. It's not just reactive, it’s reckless.

Anecdotal Evidence: He Said, She Said

Anecdotal evidence is any information based on personal experience or isolated examples instead of systematic, verified data. It’s the cousin of “vibes-based decision-making,” and it’s infecting everything, from the boardroom to the ballot box. It makes people believe that rare or isolated incidents are happening everywhere.

A recent example in the news: the nonstop cycle around trans student athletes. You’d think from the media volume and political heat that this is a nationwide crisis affecting thousands of youth sports leagues. But that’s not reality. The actual number of trans athletes competing in school sports is incredibly small, and many of them don’t even perform at the highest level. And yet, it has become a cultural flashpoint – not because of data, but because of visibility and emotional charge.

For those interested in the truth of the issue, John Oliver recently took a deep dive on the subject on Last Week Tonight (content warning: language) and laid out, in detail, just how much this “debate” has been built on a lot of attention over a few isolated incidents. The media and this administration have transformed this rare circumstance into a universal problem, and it’s causing real-world consequences. That’s anecdotal evidence doing its damage.

Stories get written, public opinion gets distorted, and decisions are made based on outliers instead of the majority.

Takeaways: How to Break the Cycle

We can't fully reprogram the human brain, but the more we understand it, the better we can manage bias and uncertainty. Here’s how to protect yourself (and your team) from the traps of anecdotal evidence and recency bias when making critical decisions:

1. Put numbers into context.
   When someone makes a sweeping claim or shares a big number, ask: “How often does that actually happen?” “What does this look like normally?” 

2. Don’t be your own customer.
   You don’t sell to a customer base of one, so don’t assume you speak for every customer if you’re also a user of your product. Let the market tell you what people want, not your own experience. 

3. Validate before you amplify.
   If a story or trend sounds sensational, especially if it’s emotionally charged, check to make sure multiple sources are confirming the same thing.

4. Train your team to think statistically.
   Teach your team (and clients) to evaluate the sample size behind any insight. One person’s story does not equate to actionable insight.

Check your sources.
The data doesn’t mean anything if it’s not coming from an educated source. Whether it’s an expert opinion from an industry leader, or human insights and emotional needs from your most important customer – make sure you’re getting information from the right people.