Some people may blame bad data.
Or unclear requirements.
Or stakeholders who don't understand statistics.
The real problem happens earlier: in how the question gets framed, which metrics get chosen, what comparisons get made, and what uncertainty gets ignored.
These aren't technical failures. They're interpretive ones.
The Same Data, Opposite Conclusions
Two people can look at the same dataset or dashboard. One says revenue is growing. The other says the business is deteriorating.
They're both right. And both are wrong.
The difference isn't the data. It's the frame.
One compared this quarter to last quarter. The other compared it to the plan. One looked at total revenue. The other looked at the margin. One focused on new customers. The other focused on churn.
Same numbers. Different interpretation choices. Opposite stories.
This happens constantly. Not because people are dishonest, but because interpretation precedes analysis—and most of those interpretive choices are invisible.
Four Patterns That Show Up Everywhere
Across industries, individuals, and tools, the same interpretive failures repeat:
Framing: People build their rationale before defining what decision the data should inform. The analysis happens. The relevant data emerges. Nothing changes. The problem isn't the available data—the underlying question was never challenged.
Signal: Metrics survive despite being useless. "Engagement" gets tracked without definition. Revenue gets reported without margin context. Activity gets measured without connection to outcomes. The numbers look good but they aren’t future forward.
Judgment: Data selection is usually a choice about what gets attention. Sales dashboards that show revenue without churn. Product dashboards that show adoption without retention. The metrics look objective but the framing serves the rationale without validation.
Humility: Numbers look relevant when the underlying assumptions are uncertain. Preconceived notions will lead to confirmation bias. That kind of precision creates false confidence.
What In Plain Byte Does
This publication examines those interpretive choices. Not to prescribe solutions, but to surface some of the patterns people don’t question.
We look at how people can choose metrics, how comparison frames shape conclusions, what gets excluded from dashboards, and when precision obscures uncertainty.
The lens is consistent: Framing, Signal, Judgment, Humility.
This Isn't About Tools or Techniques
You won't find AI Prompts or SQL tutorials here or recommendations for which platform to use for research or why.
This is for people who've realized their biggest analytical misperception didn't happen in the data they’re using. They happened in how the problem got framed, which metrics got selected, what got compared to what.
Who This Is For
- •Product managers who build metrics and wonder if they're measuring what matters.
- •Marketing analysts who track campaigns and question whether activity equals impact.
- •Operations people who design dashboards and notice they inform nothing.
- •Founders doing their own analysis and suspect their comfort zones are showing up in their metrics.
- •Casual readers who wonder if their opinions are skewed by selective data.
What You May Recognize
A sales dashboard that only shows good news. An engagement metric tracked for two years without anyone defining what it means. A hospital efficiency metric that made wait times worse. A growth projection based on three months of data reported as if it's certain.
These aren't outlier cases. They're common patterns in how people interpret raw data and ambiguous information.
The goal isn't to feel bad about it. It's to recognize the pattern before you repeat it.
The Four Pillars, Repeatedly
Each week, we examine one of these patterns through the four-pillar lens:
Some weeks introduce concepts: What is question debt? How does proxy collapse happen? When does metric advocacy show up?
Other weeks show examples: Five dashboards that serve their creators. Five numbers that look certain but aren't.
Over time, the framework becomes recognizable. You start seeing question debt in your own projects. You notice when specific criteria become advocacy. You catch false precision before you depend on it.
Pattern recognition, built through repetition.
In Plain Byte publishes weekly. One concept or set of examples examining how interpretation shapes outcomes in data work.
If you've ever wondered whether you're answering the right questions—or just the obvious ones—this might be for you.