Red Flags in Data: What to Look For

Let’s be real — data is everywhere. On your phone, in your inbox, tucked into pie charts during boring meetings. But not all data is trustworthy. Sometimes, data has ✨ red flag energy ✨ — and it’s our job to spot it before we run off making decisions based on it.

So how can you tell when something’s off? Let’s break it down.

1. 🚩 No Source, No Story

If someone’s throwing around numbers with no source in sight — be skeptical. “80% of people prefer this brand” sounds convincing… until you ask, “Says who?” If there’s no link, citation, or explanation of where the data came from, it’s giving ✨ trust issues.✨

2. 🚩 It’s Too Perfect

If the data paints a picture that’s a little too flawless, be cautious. Real-world data is messy. Perfect success rates, zero variance, or overly convenient trends? It might be cherry-picked or “cleaned” to the point of fiction.

3. 🚩 The Y-Axis Shenanigans

Graphs love drama. One way they stir it up? Messing with the y-axis. If a chart makes a tiny difference look like a massive jump, check the scale. A stretched or squished axis can twist the truth faster than a reality TV edit.

4. 🚩 Big Claims, Tiny Sample

You can’t claim “most people” think something based on 12 survey responses. Okay, technically you can, but should you? Absolutely not. Always ask: how many people? Where are they from? Is it a good representation of the group in question?

5. 🚩 Correlation ≠ Causation (Still.)

Just because two things happen together doesn’t mean one caused the other. Ice cream sales and drowning both spike in summer — but one isn’t causing the other (unless your cone is cursed). Beware of data that jumps to conclusions.

6. 🚩 It’s Missing Context

Data without context is like a tweet without a thread — too easy to misinterpret. If you see a stat and think, “Wait… compared to what?” — trust that instinct. A single number rarely tells the whole story.

So, What Should You Do?

🔍 Ask questions.

🧠 Use common sense.

📊 Double-check the source, the sample, the scale.

You don’t have to be a data scientist to sniff out shady stats — just a curious human who knows when something seems off.

Because when data gets dramatic, we stay grounded.

As always, stay curious ✨