If you’ve ever run an A/B test in your PPC campaigns – testing two ads, two landing pages, or even two audiences – you’ve probably faced the question:
👉 “Is the difference I see real, or just random chance?”
That’s where the p-value comes in.
Imagine you’re running Google Ads and see these results:
Ad A: 100 clicks → 5 conversions (5% CR)
Ad B: 100 clicks → 7 conversions (7% CR)
Ad B looks better, right? But what if those 2 extra conversions happened just by luck? Maybe tomorrow Ad A will catch up. Without statistics, you can’t tell whether B is truly better or if it’s just noise in the data.
The p-value is simply a probability score that tells you:
Low p-value (usually < 0.05) → The difference you see is likely real.
High p-value (> 0.05) → The difference could just be random chance.
Think of it like this:
If p = 0.03 → only a 3% chance the difference is random.
If p = 0.30 → a 30% chance it’s random → too risky to make a decision.
When you test two ads or landing pages, you want to know whether one really performs better in terms of Conversion Rate (CR).
This is where the Chi-Square test comes in. It’s a statistical method that compares how many people converted in each group vs. how many didn’t.
If the Chi-Square test gives a low p-value, you can be confident one version is truly better.
If the p-value is high, you shouldn’t make decisions yet – you probably need more data.
Ad A: 1,000 clicks → 50 conversions (5%)
Ad B: 1,000 clicks → 70 conversions (7%)
You run a Chi-Square test, and it gives:
p-value = 0.02
That means there’s only a 2% chance the difference is random.
You can safely conclude Ad B wins, and scale it with confidence.
Ad A: 100 clicks → 5 conversions (5%)
Ad B: 100 clicks → 6 conversions (6%)
The CR increased from 5% → 6%, so it seems Ad B is better. But when you run a Chi-Square test:
p-value = 0.62
This is a high p-value, meaning there’s a 62% chance this difference is just random.
Even though it looks like Ad B is winning, you cannot be confident it’s actually better. Making changes based on this small difference could waste your budget.
Save Money – Don’t waste budget on “winners” that only looked good by accident.
Make Confident Decisions – Know when results are solid enough to act on.
Improve Campaigns Faster – Identify truly better ads, audiences, or landing pages.
The p-value isn’t about complex math – it’s a confidence tool.
It tells you if your A/B test result is real or just random.
With the Chi-Square test, you can compare conversion rates between ads, audiences, or dimensions and make data-driven decisions.
Next time you see a conversion rate increase, remember: it may not be real until the p-value confirms it.
On the Internet, you can find many marketing cases claiming that males convert 50% better than females, leading to decisions to create separate campaigns for this audience and reallocate more budget accordingly. Or, you might see statements like: our best-performing age group is 35-44 years old, while the worst is 55+, so we decided to turn off the latter due to budget limitations.
I have always wondered how to include both clicks and conversions in my analysis since I need to consider both lead quantity and cost efficiency.
Here in the GitHub project, I'm exploring age, gender, device dimensions, and their statistically significant differences between each other
https://github.com/BookerSK/Statistically_approved/blob/main/Chi_Square.ipynb