You spent weeks on creating a marketing campaign where you’ll test different ads, diligently researched competitors, went back and forth with the design team and you finally got a green light – only to have it rejected by the data team because the results won’t be statistically significant…
Why the backlash? What’s the resistance? You have thousands of recipients for each different advertisement… surely that is enough to test which creative gets you the best results?
Let’s dig a little deeper into the thought processes of your data team on how they understand statistical significance and how it should be used in marketing.
What are the basics of statistical significance?
In marketing analytics statistical significance helps you determine whether the results you observe in your test are due to a real effect – or due to random chance.
Having a statistical significance in your test is important because it helps you determine whether the effect you see holds up in a broader context. If the results are not statistically significant you may risk making decisions on results that may not hold up over time.
Why hypotheses are important in the context of statistical significance
When you boil it down to the core, statistical significance says something about the hypotheses of your test. For each test you should have a null hypothesis and an alternative hypothesis. Let’s look at an example for a A/B test:
Your null hypothesis assumes that there is no difference in effect between variant A and variant B.
Your alternative hypothesis assumes that there is a difference in effect between variant A and variant B.
Based on the results of your A/B test you will probably have two different groups, with two different conversion rates. Your data analist will then use a specific method to test for statistical significance and come to you with a ‘p-value’. But what does that p-value mean exactly you may think?
Why the p-value is important in the context of statistical significance
The p-value will tell you the likelihood of the test result being due to chance. If your p-value is for example 0.10, the chance of your test result being due to chance is 10%. On the other hand, if your p-value is for example 0.001, the chance of your test result being due to chance is 0.01%. The latter will give you a much better basis for an informed decision on whether to continue with option A or option B.
Pro tip: A common misconception is that statistical significance proves a result is important or practically valuable—when, in fact, it only tells us whether an effect is unlikely to be random.
Additionally, a significant result doesn’t always imply large or meaningful differences in practice. Understanding these basics is key to avoiding overreliance on statistical significance alone.
What significance threshold (p-value) to use?
Now you know that the p-value indicates the likelyhood of your test results being due to chance. But what cutoff point should be used to determine whether or not a test is significant?
There are different ways to go about that in marketing.
Most of the time when you hear that a test is statistically significant it means that the p-value is below 0.05 – meaning that the chance of the test result being due to chance is 5%.
However, you should not always have to use this cut-off point. You need to take the context of your test into account. For instance:
In a high-risk scenario (e.g., launching a costly nationwide campaign), you might want a more stringent threshold like 0.01 to reduce the chance of making a wrong decision.
However, for exploratory tests or low-cost experiments, a higher threshold like 0.1 might be acceptable, as the consequences of acting on falsely positive result are less severe.
How should marketers use p-values?
Marketers commonly use p-values in A/B testing to evaluate the impact of different variations, such as ad copy, website layouts, or email subject lines.
For example, a marketer might compare two versions of a landing page—one with a bold call-to-action button and another with a more subtle design—to identify which drives higher conversions. The p-value helps assess whether the observed difference in performance is likely due to a real effect or just random chance.
However, statistical significance does not always equate to practical significance. While a low p-value (typically below 0.05) indicates the observed difference is unlikely due to chance, it doesn’t necessarily mean the difference is large enough to justify action.
For instance, a test result showing a statistically significant 0.5% increase in click-through rate might not generate enough incremental revenue to offset the costs of implementing the change.
Marketers should also consider the effect size—the magnitude of the difference between variations—to determine whether the result has meaningful business implications.
Acting solely on statistical significance, without evaluating practical relevance, can lead to wasted resources or changes that fail to deliver a positive return on investment.
What are the limitations of p-values? (And how to prevent them)
Using p-values and statistical significance to evaluate your test results does not come wihtout it’s limitations. And if you don’t know it’s limitations it may lead you to make the wrong conclusions from a test.
P-hacking
P-hacking occurs when someone intentionally or unintentionally manipulates data, analysis methods, or test conditions to produce a statistically significant result.
How to prevent it: Before you start your test, clearly define what you’re trying to find out (your hypothesis) and how you plan to analyze the results. Stick to your original plan instead of trying out lots of different ways to look at the data just to find something significant—this can lead to misleading results.
Small sample sizes
Tests with insufficient sample sizes can produce unreliable p-values. Small samples increase the chance of both false positives (significant results that are not real) and false negatives (missing actual effects).
How to Prevent It: Calculate the required sample size before running the test to ensure adequate statistical power. Use a power analysis tool to estimate the minimum sample size needed to detect a meaningful effect. Avoid prematurely stopping a test once significance is reached.
Binary interpretation
Treating p-values as a strict cutoff (e.g., below 0.05 = significant, above 0.05 = not significant) oversimplifies the results and ignores the nuance of probability.
How to Prevent It: Interpret p-values as part of a broader decision-making framework. Consider the context, confidence intervals, and robustness of the findings. Avoid focusing solely on whether a result is “significant” and evaluate the broader picture of your data.
3 Practical tips you can immediately implement as a marketer
1. Align tests with business goals
Before running a test, ask yourself: What am I hoping to achieve, and how will this test impact my business? Focus on testing changes that can significantly influence key metrics like revenue, conversions, or engagement.
At all times you should avoid overanalyzing minor differences that won’t move the needle for your business, even if they’re statistically significant. This only takes up time and resources that won’t lead to any meaningful difference.
2. Focus on sample size and timing
A test with too few participants or too low of a conversion rate can lead to unreliable results. Aim for a sample size that reflects your typical audience behavior and gives enough data to detect meaningful changes.
The online tool allows you e.g. to calculate the necessary test group sizes you need for a certain minimum detectable effect as well as well as a calculator to calculate the p-value based on your test results.
3. Combine statistical significance with your own insights
When interpreting results, consider whether the observed change is large enough to justify action. Use statistical results as one input in your decision-making process, alongside business metrics and expert intuition.
This approach helps ensure your actions are both data-driven and strategically sound. If a test shows a statistically significant 1% lift in click-through rates but implementing the change is costly or time-consuming, weigh the potential ROI before making a decision.
Conclusion
Understanding statistical significance can transform how you approach marketing campaigns and collaborate with your data team. While it might feel frustrating when a promising idea is paused or questioned, remember that the goal of statistical rigor isn’t to block creativity—it’s to ensure your efforts lead to meaningful, reliable outcomes.
By considering factors like sample size, effect size, and business context, you can design campaigns that not only resonate with your audience but also yield results you can trust.
Statistical significance is a powerful tool, but it works best when paired with practical judgment and clear business goals. When marketers and data teams align on these principles, you’re not just testing ads—you’re building a smarter, more impactful marketing strategy.
