My experience with Parkinson’s law
Ideally 55-60 characters
Why Your Data Is Lying to You (And How to Spot It)
4 identical datasets that will change how you make decisions forever.
TABLE OF CONTENTS
Your spreadsheet shows identical numbers. Same averages. Same correlations. Same everything.
But you're about to make a huge mistake.
Last week, I was reviewing some research data when something struck me as odd. The numbers all looked the same—identical averages, identical correlations, identical everything. Yet when I graphed them, the patterns couldn't have been more different. One showed a perfect straight line. Another curved like a smile. The third had a clear outlier throwing everything off. The fourth? It defied logic entirely.
This wasn't a mistake. It was Anscombe's Quartet in action—one of the most powerful demonstrations of why we need to look beyond the surface of information to make better decisions.
The Deceptive Power of Summary Statistics
In 1973, statistician Francis Anscombe created four datasets that would forever change how we think about data.
Each dataset contained eleven points with identical statistical properties: the same mean for x and y values, the same correlation coefficient, and the same regression line. On paper, they were mathematically twins.
The summary statistics show that the means and the variances were identical for x and y across the groups :
- Mean of x is 9 and mean of y is 7.50 for each dataset.
- Similarly, the variance of x is 11 and variance of y is 4.13 for each dataset
- The correlation coefficient (how strong a relationship is between two variables) between x and y is 0.816 for each dataset.
But when you graph them:
- Dataset 1: Perfect straight line (predictable relationship)
- Dataset 2: Clear curve (non-linear pattern)
- Dataset 3: Straight line + one massive outlier (distorted results)
- Dataset 4: No relationship at all (random noise)
Conclusion: Four completely different datasets can have identical descriptive statistics. Your summary statistics are blind to the patterns that actually matter.
Why Our Brains Fall for Statistical Shortcuts
We live in an age of information overload. Every day, we're bombarded with statistics, metrics, and summaries designed to help us make sense of complex realities. The average salary in our industry. The correlation between exercise and happiness. The return on investment for marketing campaigns. These numbers feel concrete, trustworthy, and actionable.
But Anscombe's Quartet reveals a fundamental flaw in how we process information: we trust summaries without examining the underlying patterns.
This happens because our brains are efficiency machines. When faced with complexity, we naturally gravitate toward shortcuts—what psychologists call heuristics. A single number feels manageable. A correlation coefficient gives us confidence. An average provides clarity. These mental shortcuts serve us well in many situations, but they can also blind us to crucial details that change everything.
Where This Goes Wrong in Real Business
Hiring: Two candidates with identical test scores. One is consistently good across all skills. The other is brilliant at some things, terrible at others. Same average. Completely different employees.
Sales Funnels: Two lead magnets with identical conversion rates. Lead magnet A brings consistently engaged subscribers who open every email. Lead magnet B attracts mostly freebie seekers who unsubscribe after getting your bonus.
Content Performance: Two pieces of content with identical engagement rates. Content piece A builds steady, loyal following over time. Content piece B gets one viral moment then fades into irrelevance.
Email Marketing: Two email sequences with identical open rates. Sequence A nurtures subscribers who eventually become high-value customers. Sequence B gets attention but never converts to sales.
Operations: Two processes with identical average completion times. Process A is reliable. Process B randomly breaks down, creating customer service nightmares.
The pattern: Averages hide the story your business actually needs to hear.
The Visualization Imperative
Anscombe's lesson is elegantly simple: always look at your data, don't just summarize it. But this principle extends far beyond statistics into every area of decision-making.
When we visualize information—whether through graphs, charts, or even mental models—we engage different parts of our cognitive machinery.
When we visualize information—whether through graphs, charts, or even mental models—we engage different parts of our cognitive machinery.
Our visual processing system excels at pattern recognition in ways that our analytical mind cannot match.
A quick glance at Anscombe's four graphs reveals truths that hours of statistical analysis might miss.
This isn't just about data visualization. It's about developing what we might call "pattern literacy"—the ability to see beyond surface-level metrics to understand underlying structures and relationships.
This isn't just about data visualization. It's about developing what we might call "pattern literacy"—the ability to see beyond surface-level metrics to understand underlying structures and relationships.
Before making any data-driven decision, ask yourself:
"What would this look like as a graph?"
90% of bad business decisions come from trusting summaries without seeing patterns.
Here's your new decision-making process:
- Get the summary stats (what you're probably doing now)
- Graph the actual data (what you're probably skipping)
- Look for patterns that contradict the summary (where the gold is hidden)
- Make decisions based on patterns, not averages (how winners think)
The Three Patterns That Matter Most
Pattern 1: The Outlier Effect
One extreme data point skews everything. Your "successful" campaign might be carried by one viral post that will never happen again.
One extreme data point skews everything. Your "successful" campaign might be carried by one viral post that will never happen again.
Pattern 2: The Timing Effect
Your averages ignore when things happen. Seasonal businesses can't rely on annual averages. Growth companies can't trust last year's metrics.
Your averages ignore when things happen. Seasonal businesses can't rely on annual averages. Growth companies can't trust last year's metrics.
Pattern 3: The Distribution Effect
Two teams with the same average performance could have completely different consistency levels. One delivers reliably. The other swings between genius and disaster.
Two teams with the same average performance could have completely different consistency levels. One delivers reliably. The other swings between genius and disaster.
How Top Performers Actually Use Data
Level 1 Thinking: "My average cost per lead across all platforms is $2.50."
Level 2 Thinking: "Platform A delivers steady $2.50 leads daily (reliable). Platform B swings between $1.00 viral leads and $5.00 dead-zone leads (unpredictable). Platform C appears steady but relies on one $0.50 viral moment that won't repeat (unsustainable). Platform D shows random $1.00-$4.00 costs with no pattern (unreliable)."
Level 3 Thinking: "Platform A's steady performance suggests systematic audience fit - I should scale here first. Platform B's volatility indicates algorithm dependency - useful for testing but dangerous for primary growth. Platform C's outlier suggests content-market fit happened once - I need to reverse-engineer what worked. Platform D shows no systematic relationship - cut spending immediately."
The difference? Level 1 sees averages. Level 2 sees patterns. Level 3 sees systems.
Your Action Plan
This Week:
- Take your three most important business metrics
- Graph the underlying data (not just the summaries)
- Look for patterns your averages might be hiding
This Month:
- Audit your dashboard - replace averages with pattern-revealing visualizations
- Train your team to ask "What does the graph look like?" before making decisions
- Build pattern recognition into your decision-making process
This Quarter:
- Identify the top 3 decisions where you trusted averages over patterns
- Calculate what those decisions actually cost you
- Create systems to catch pattern-blind decisions before they happen
The Compound Effect of Pattern Thinking
Every time you look beyond the summary to see the pattern, you build better business instincts.
You start noticing:
- Which customers are truly loyal vs. temporarily convenient
- Which marketing efforts create sustainable growth vs. lucky breaks
- Which team members deliver consistency vs. occasional heroics
- Which strategies work because of skill vs. circumstance
This isn't just about being more analytical. It's about being more honest with yourself about what's actually working.
The Bottom Line
Anscombe's Quartet teaches us the most expensive lesson in business:
Identical numbers can tell completely opposite stories.
Your spreadsheet might say two strategies are performing equally. But one could be building sustainable competitive advantage while the other is burning through luck.
The winners aren't the ones with better data. They're the ones who see patterns others miss.
Next time someone shows you a compelling average or correlation, remember Anscombe's lesson:
Ask to see the graph.
Your future self will thank you.
Related Posts
The Wright Way to Innovate: How Defying Patterns Can Launch Your Next Big Idea
Discover how the Wright brothers defied conventional wisdom to achieve powered flight, and learn actionable steps to break patterns, think innovatively, and turn bold ideas into reality.