Statistical Checks: What “Significant” Really Means

Why this issue matters:

“Significant” is often mistaken for “important”
P-values are used everywhere, yet misread every week.
Executives do not buy rituals, they buy clear risk and clear payoff.
Used well, p-values help you act with confidence. Used poorly, they waste money and trust.

Good day to you one and all!

Over the past few weeks, Aparna Joseph has been demystifying the statistical habits that protect credibility when questions come fast. She has shown how to communicate uncertainty in a way leaders can actually use.

Today, Aparna goes deep on p-values, what they really say, what they never say, and how to make them decision-ready in the room.

Lets go!

Make “significance” honest: what p-values really mean in the room

by Aparna Joseph (LinkedIn)

If you've worked with A/B tests, ran a regression, or even taken a stats class, you've likely come across p-values. They're everywhere in data analysis yet still widely misunderstood.

Despite how often we use them, p-values are rarely explained clearly. Many professionals apply them routinely without fully grasping what they measure, how they should be interpreted, or when they actually matter.

In this article, we’ll unpack what p-values actually represent, why confusion around them is so common, and how they remain relevant in modern data science, even in a landscape dominated by machine learning and large-scale automation.

What Exactly Is a P-value?

A p-value helps us evaluate how well our data supports a particular assumption. Usually the "null hypothesis" which often represents the idea that nothing interesting is going on (no difference, no effect, no relationship).

In plain terms, a p-value answers the question:

"If there really were no effect, how likely would it be to see results this extreme?"

When is a p-value considered small?

If the p-value is below a commonly used threshold like 0.05, it suggests that your observed results would be quite rare under the assumption of no effect. That gives you reason to question the null hypothesis, and consider that something real might be happening.

What about large p-values?

If your p-value is high, that means your observed data could easily occur by random chance if the null hypothesis were true. It doesn’t prove there’s no effect just that your data doesn’t offer strong evidence of one.

What p-values don’t tell you:

• Whether the effect is large or meaningful

• Whether your hypothesis is correct

• Whether the result would repeat in the future

Why P-values Still Matter in Data Science

Even in an era where machine learning and automated decision systems are increasingly common, there are still many practical cases where p-values offer real value.. especially for making decisions based on evidence rather than assumptions.

Here are a few common places where p-values still earn their place:1. A/B Testing and Experimentation

Running an A/B test to compare two versions of a feature or webpage? A p-value tells you whether the difference in outcomes (like click rates or conversions) is likely due to random noise or something more meaningful.

Without a statistical check like this, it’s easy to act on results that are just fluctuations in the data not actual improvements.

2. Regression and Feature Significance

In models like linear or logistic regression, p-values are often used to assess whether a particular variable is genuinely contributing to the outcome or is just noise.

This is especially useful when explaining model outputs to stakeholders or when building

interpretable models. P-values can help answer questions like:

• Which features are driving this prediction?

• Can we trust the influence of this variable?

3. Hypothesis-Driven Analysis

A lot of data work isn’t about prediction. It’s about understanding.

Whether you’re investigating if a marketing campaign worked, or whether behavior differs across user segments, p-values offer a way to test those ideas systematically.

They provide structure when turning business questions into statistical analysis.

Common Misunderstandings About P-values

P-values are often misunderstood, and that can lead to bad decisions. Let’s clear up three of the most common misconceptions:

"If the p-value is below 0.05, the result must be important"

Not quite. A small p-value tells you the result is statistically unlikely under the null hypothesis, but it says nothing about the size or relevance of the effect. You can have statistical significance without practical value.

"A p-value above 0.05 means there’s no effect"

Actually, it just means the evidence isn’t strong enough to reject the null hypothesis. The effect might still exist, but your data — perhaps due to small sample size or variability — isn’t sufficient to say so confidently.

"The smaller the p-value, the better"

This isn’t always true. With large datasets, even tiny, meaningless differences can produce very small p-values. So a p-value of 0.00001 might look impressive, but that doesn’t mean the effect is worth acting on.

Best Practices for Using P-values

Used wisely, p-values are powerful. But they should be applied with care and context. Here are four key guidelines:

1. Don’t use p-values in isolation

Always pair them with effect sizes or confidence intervals. A significant p-value tells you if an effect is likely real, but not how big or how meaningful it is.

2. Account for multiple comparisons

If you test lots of variables or segments, you're more likely to find a small p-value by chance. Use corrections (like Bonferroni) or control for false discovery rates.

3. Focus on the question, not the threshold

The common 0.05 threshold is a guideline, not a rule. Ask:

"Is this result compelling and relevant?" rather than just "Did it cross the 0.05 line"?

4. Keep your communication clear

When talking with anyone other than a statistician .. avoid saying,

"the p-value is 0.041"

Instead say,

"This result is statistically significant, which means it’s unlikely due to random chance"

Takeaways

P-values help answer an important question: Is this pattern in the data likely to be real, or just random? They’re widely used for good reason, when interpreted correctly, they’re a valuable tool for data-driven reasoning.

They’re especially helpful when:

• Testing a hypothesis or comparing groups

• Interpreting¹ model coefficients

• Deciding whether to act on experimental results

But they’re not the whole story. Use them alongside confidence intervals, effect sizes, and practical judgment. And always frame your findings in a way that focuses on what’s meaningful, not just what’s statistically significant.

The Friday Checklist

• Pair every p-value with effect size and a confidence interval

• Report the exact p (for example, p = 0.041), state alpha, and name the test used.

• Declare the number of comparisons, and correct or justify why not

• Translate “statistically significant” into a business action, or say, “hold, collect more data”

• Avoid binary thinking. State what the current evidence supports, and what would change your call

Words you can lift into your comms

KPI update
“We saw a 0.6 percentage point lift in sign-ups, 95% CI 0.1 to 1.1, p = 0.03. The effect is small but real at our threshold. We will roll to 30% traffic and keep monitoring”
Executive translation: The improvement is likely real, but modest. We will expand cautiously to limit risk.

Exec readout
“In the churn model, contract length and first-month usage remain significant contributors after controls, p < 0.01, with practical effects aligned to our retention levers”
Executive translation: These factors matter in ways we can act on. Keep the levers in the plan.

Budget pitch
“The campaign’s lift is statistically reliable, p = 0.02, and the effect size clears our ROI hurdle at current CAC. Funding an expanded test is justified”
Executive translation: The result is unlikely to be random noise, and the size is big enough to pay. Approve a bigger run.

Pitfalls to avoid

• Treating p < 0.05 as “important” without size, cost, or risk.

• Declaring “no effect” on a noisy, under-powered sample.

• Chasing tiny p-values in giant datasets while ignoring practical significance.

• Running many tests without correction, then celebrating the first small p you find.

I am learning as I go, and my aim is to make evidence easier to act on.
I hope this helped you turn “significant” into decisions you can defend.
- Aparna

A note from Tom..

I want to express my gratitude and thanks to Aparna for joining us for the last 4 weeks in this series to deep dive on the importance of statistical checks. I have -always- had a data scientist, mathematician or statistician on our analytic teams. And these four articles show exactly why it is important to have this firepower on hand to help turn data into meaningful decisions.

Thank you Aparna for taking the time to put together this series, and helping our readers understand this super important topic!.

I wish you all the best in your bright future.

Best,

Tom.

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