Statistical Checks: The Data Literacy Skill Nobody Talks About

Good day to you one and all!

I’ve asked Aparna Joseph, a data scientist completing her master’s, to break down one of the most mystical yet critical parts of analytics work: statistical checks.

She bridges two worlds: the technical detail data scientists live in and the practical reality every other role in a project needs to understand.

Her point is simple: checks aren’t about how to crunch the numbers, they’re about why your analysis holds up when it’s under fire. Skip them, and even the most polished deck can quietly steer a project off a cliff.

Here’s Aparna with the first in a short series: a piece that should be required reading for anyone who touches data.

by Aparna Joseph (LinkedIn)

You’ve been in that meeting. The slides are polished. The charts are neat. The story sounds obvious.

And yet, something in the back of your mind says: “Do we really know this is true”?

In data projects, confidence can be dangerous. Numbers can be persuasive even when they’re wrong. Patterns can be convincing even when they’re pure coincidence. And if you’ve ever watched a project sprint full-speed into the wrong decision, you know the cost.

Today, I’m introducing article 1 of a multi part series breaking down why statistical checks aren’t just for data scientists - they are for everyone who touches a data project.

In fact, they’re part of the basic data literacy every modern professional should have. Because if you can understand why they matter, you can ask better questions, spot shaky analysis, and protect your organisation from expensive mistakes.

By the end, you’ll know:
• Why they’re the safety net you didn’t know you needed
• Where they fit in the process so you can spot problems early
• How they protect your credibility when the numbers are on the line

Lets go!

Why They’re Worth Your Attention

You’ve probably been there. The presentation is polished, the charts are tidy, the story sounds convincing. Everyone’s nodding along.

But there’s that little voice in your head (again!): “Do we actually know this is true?”

In data work, confidence can be dangerous. Numbers can look compelling even when they’re wrong. Patterns can seem clear when they’re nothing more than random chance. And the cost of acting on them can be huge; wasted spend, missed opportunities, and decisions that send the business in the wrong direction.

Statistical checks are the safety net. And they’re not just for data scientists, they’re part of basic data literacy.

Every manager, analyst, and decision-maker should understand them well enough to ask the right questions.

How They Protect You

Sometimes numbers tell a story you want to believe. Let’s say sales jump right after a product change. Without testing, you can’t know if the change really caused the jump, or if it was seasonal demand, a competitor’s slip-up, or just plain luck. Checks separate the signal from noise so you’re not building strategy on coincidence.

They also guard against the “beautifully wrong” model. One that produces impressive results for all the wrong reasons. A model can look like it’s performing brilliantly, but if it’s making predictions based on quirks in the data rather than real relationships, those predictions will fall apart when the context changes.

Statistical checks are how you uncover that before it costs you.

Every analytical model comes with assumptions. Even the simplest regression expects that errors are random, that inputs aren’t duplicating the same information, and that relationships between variables stay stable over time.

If any of these break, your results can quietly drift from reality. Without checking, you might never notice (until the business starts making decisions based on bad numbers).

Checks also cut through “variable clutter”. It’s common to have fields or metrics in your dataset that look important but add no real predictive power. Worse, some variables may conflict with others or create redundancy that confuses your analysis. Testing each one for genuine contribution keeps the work lean and focused.

And then there’s the problem of overconfidence.

Predictions always come with uncertainty, yet many reports present a single, definitive number. Checks let you express that uncertainty honestly .. “We expect between 1,150 and 1,250 sign-ups, with 95% confidence”.

Which is far more credible and useful than pretending you know exactly what will happen.

Where They Fit in the Process

The biggest mistake is treating statistical checks as a final “tick box” step.

They belong at multiple points in a project, each one protecting you from a different kind of risk.

When you first explore the data (EDA): This is where you discover whether the foundations are solid. Checks at this stage tell you if your variables are normally distributed, reveal extreme outliers that could distort results, and highlight strong correlations that might indicate duplication or bias.

Catching these early saves hours of wasted modelling later.

When choosing which factors to include (feature selection): This is about making sure you’re focusing on the right inputs. A quick test might show, for example, that age has no meaningful effect on click rate, or that two different product preference fields are actually telling you the same thing.

Removing irrelevant or redundant features keeps your model simple and robust.

While building the model: At this stage, checks verify that the method you’ve chosen actually fits the data. Are the errors distributed the way they should be? Is the variance in predictions consistent, or does it explode in certain ranges? For time series models, are the errors independent over time, or are you seeing patterns that suggest the model is missing something?

These answers determine whether you can trust the results.

When comparing models: Imagine you’ve built two models and one outperforms the other by 2%. That might sound decisive — but is it a real difference or just a random fluctuation?

Statistical checks let you compare performance rigorously, and express your confidence in the choice you make.

When running experiments or A/B tests: This is where statistical checks are non-negotiable. You might see that one version of a product has a 4% higher conversion rate than another. Without proper testing, that could be nothing more than chance. Checks confirm whether the difference is statistically significant, help you avoid false positives, and guide you in designing the test with enough sample size to get a reliable answer.

At each of these points, the checks are doing the same job. Protecting you from acting on results that look convincing but aren’t actually true.

Have I painted the picture for you? Do you now understand what you may have been missing this far?

Three Questions That Change the Conversation

As a Data professional - You don’t have to run the tests yourself, but you do need to know enough to expect them - and to notice when they’ve been skipped.

Ask these three questions whenever you’re shown results:

1. How do we know this isn’t just random?

2. What assumptions were made, and have they been checked?

3. How confident are we in these numbers and what’s the range?

If those can’t be answered clearly, you’re not making a decision based on evidence.

You’re making it on hope, and that’s no way to run a project.

Common Myths (That Hurt Good Projects)

• “The data looks clean, so it must be fine.”
  Even perfect-looking data can mislead without testing.

• “High accuracy means it’s right.”
  Accuracy can hide overfitting, leakage, or broken assumptions.

• “This is for researchers, not us.”
  If your work affects decisions, you need it.

• “If it works in production, it’s valid.”
  It might work now, but for the wrong reason — and break later.

The Real Reason This Matters

Statistical checks aren’t about slowing you down. They’re about ensuring:
• The patterns you see are real
• The insights you share are credible
• The decisions based on your work are sound

And they’re part of the core data literacy skill set that every leader, project manager, and analyst should understand, even if they never run the tests themselves.

Because if you know what these checks are meant to do, you can have sharper conversations with your data team, challenge assumptions respectfully, and avoid being blindsided by an insight that falls apart under scrutiny.

That’s why you need someone in the loop who knows how to run these checks and interpret them correctly. Without that expertise, you’re building your decisions on sand.

If you want to be trusted - whether you’re an analyst, a project lead, or a decision-maker - this isn’t optional.

It’s the difference between analytics that looks good, and analytics that holds up.

I’m learning a lot as I grow in this field, and sharing what’s helped me think more clearly.
Thanks for reading, I hope this gave you something useful to take with you.
- Aparna

Next week: I’ll show you how to choose the right statistical test for your situation, with plain-English explanations and a simple decision flow that anyone can use.

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