As a datadriven marketer, you‘re not just responsible for measuring the results of your campaigns and experiments, but also demonstrating the validity and reliability of those results. That‘s where statistical significance comes in.
Statistical significance is a way to mathematically prove that a result or relationship you observe in your marketing data is not likely to occur randomly or by chance. In other words, it provides an objective measure of whether a difference you see is "real" and can be attributed to the factors you are testing.
While statistical significance is a core concept in statistics, many marketers struggle to fully grasp what it means and how to properly apply it in their work. Some common misconceptions and mistakes include:
 Thinking that statistical significance is the same thing as business impact or practical significance
 Assuming that a "significant" result automatically means it‘s a strong or important effect
 Not considering the role of sample size and statistical power in detecting significance
 Misinterpreting pvalues as probabilities that a hypothesis is true or false
 "Phacking" or manipulating data and analysis to find "significant" results
 Making decisions based on individual significant tests while ignoring the multiple comparisons problem
To help you thoroughly understand statistical significance and steer clear of these pitfalls, we‘ve put together this comprehensive guide. We‘ll walk through what statistical significance really tells you, how to calculate it, and best practices for using it in your marketing experiments and data analysis. Let‘s dive in!
What Statistical Significance Tells You (and What It Doesn‘t)
The technical definition of statistical significance is that an observed result is unlikely to have occurred by random chance. More specifically, it usually means that the probability (pvalue) of obtaining the observed effect if the null hypothesis were true is less than a predefined significance level (typically 5% or 1%).
If that sounds complex, here‘s a simple way to think about it:
Statistical significance is a way to assess the likelihood that the difference between two groups or the relationship between two variables is real and not just due to random noise or error in your data.
For example, let‘s say you run an A/B test on your website comparing the conversion rates of two different headlines. You observe that Headline A has a 5.2% conversion rate while Headline B has a 5.5% conversion rate.
Is that difference in conversion rates large enough to conclude that Headline B is really outperforming Headline A? Or is it possible that the variation you‘re seeing is just due to random chance and that there‘s no real difference between the two headlines‘ performance?
That‘s where statistical significance comes in. By calculating statistical significance, you can determine the probability that the observed difference in conversion rates was due to chance. If the probability is low (typically less than 5%), then you can conclude that the difference is statistically significant and likely represents a real effect.
However, it‘s critical to understand that statistical significance does NOT tell you:
 How large or strong an effect is
 Whether an effect is practically significant or has a meaningful impact on your business KPIs
 That a finding is important, valuable, or worth acting on
As an extreme example, imagine that you have an enormous sample size for your A/B test due to high traffic volumes. You might find that a miniscule difference in conversion rates of 0.01 percentage points is statistically significant with a tiny pvalue.
But does that mean you should rush to implement the "winning" headline? Probably not, since the effect on actual conversions would be minimal. It‘s statistically significant, but not practically significant to your marketing goals.
This is why it‘s important to consider not just statistical significance, but also the size of the effects you are measuring and whether they align with your business objectives. Effective marketers need to use statistical significance as one tool in their experimentation toolbox, but not the only one.
How to Calculate Statistical Significance for Your Marketing Experiments
There are several different approaches to calculating statistical significance depending on the type of data you have and the questions you‘re trying to answer. The most common method in marketing is null hypothesis significance testing (NHST) using pvalues.
The basic process of NHST is:

Define your null hypothesis (H0) and alternative hypothesis (H1). The null hypothesis usually states that there is no effect or relationship between variables, while the alternative hypothesis states that there is an effect.

Collect your data and calculate a test statistic that quantifies the difference or effect you observed.

Use the test statistic and the sample size to calculate a pvalue, which estimates the probability of observing results at least as extreme as your data if the null hypothesis is true.

Compare your pvalue to a predefined significance level (α). If p < α, you "reject the null hypothesis" and conclude the effect is statistically significant. If p ≥ α, you "fail to reject the null hypothesis."
Common statistical tests used in marketing include:

Chisquare test: Used for testing relationships between two categorical variables, such as comparing the clickthrough rates of two ad versions.

Ttest: Used for comparing means between two groups, such as average order values or time on site.

ANOVA: Used for comparing means between three or more groups, such as engagement rates across multiple audience segments.

Regression analysis: Used for quantifying the relationships among multiple variables, such as analyzing the impact of CTA button color, page layout, and copy sentiment on conversion rates simultaneously.
The calculations for each of these tests can get a bit complex, which is why most marketers rely on software tools and calculators to handle the math. However, it‘s still valuable to understand the underlying concepts and assumptions behind the methods.
One important aspect of NHST to consider is statistical power, which is the probability that a test will detect an effect if there is one. Power depends on your sample size, effect size, and significance level. In general, larger sample sizes give you more power to detect significant differences, which is why it‘s important to make sure you have enough data before concluding a test.
There are many online calculators you can use to estimate the sample size you need for a desired level of power, or vice versa, to see if your test is sufficiently powered.
Another key concept is confidence intervals, which provide a range of plausible values for your effect size based on your sample. Unlike pvalues which are just a threshold for significance, confidence intervals give you richer information about the direction and magnitude of effects.
For example, a confidence interval for the difference in conversion rates between two ads might be [2.5%, 5.5%]. You can interpret this as being 95% confident that the true population difference falls between 2.5% and 5.5%, a much more informative statement than simply saying p < 0.05.
By understanding these nuances of NHST, you‘ll be better equipped to design robust experiments, avoid costly errors, and make sound datadriven decisions.
Best Practices for Using Statistical Significance in Marketing
With a solid grasp of what statistical significance is and how to calculate it, you can start putting it into practice in your marketing experiments and analysis. Here are some best practices and tips to keep in mind:
1. Always start with clear hypotheses and success metrics.
Before you start any marketing experiment, be sure to explicitly define your hypotheses and decide which metrics you‘ll use to evaluate performance. Your hypotheses should specify the effects you expect to see, and your success metrics should be tied directly to business goals. This will help you design the most relevant tests and interpret your results accurately.
2. Make sure your sample sizes are large enough.
As mentioned above, statistical power is critical for detecting significant effects. Don‘t waste time and resources running experiments without enough data to yield meaningful insights. Use power calculators upfront to determine the minimum sample size you‘ll need for your desired significance level and effect size.
3. Use A/A tests to check for errors and biases.
Before running an A/B test, consider first running an A/A test where you compare two identical versions of a page or asset. This can help you identify any technical errors or inconsistencies in your testing platform that could skew your results. An A/A test can also give you a baseline level of variance to expect.
4. Correct for multiple comparisons.
If you‘re running multiple tests on the same data (e.g. several different ad variations), you‘ll need to account for the multiple comparisons problem. The more tests you run, the higher the odds of seeing significant results just by chance. Methods like the Bonferroni correction and FDR control can help limit your risk of false positives.
5. Don‘t overinterpret or overgeneralize your findings.
Be careful not to read too much into your results or apply them too broadly without sufficient evidence. Significant effects in an email subject line test don‘t necessarily translate to all subject lines. Similarly, shortterm lift on one conversion metric may not indicate better longterm campaign ROI. Use multiple experiments and data sources to validate and replicate important findings.
6. Look at more than just pvalues.
While pvalues are a useful threshold for significance, they have limitations. Most notably, they don‘t directly quantify effect sizes. That‘s why it‘s a good practice to also examine confidence intervals and measures like Cohen‘s d that put the magnitude of your results into context.
7. Focus on practical and business significance, too.
Don‘t get so caught up in the statistical weeds that you lose sight of the big picture. Always loop back to what your tests mean for your actual marketing goals and KPIs. A significant lift in CTR is only valuable if it also drives more leads, conversions, revenue, or other priority metrics. Let statistics be a tool for achieving business results, not an end in itself.
8. Communicate results clearly to stakeholders.
When reporting your experiment results to clients, executives, or other stakeholders, make sure to present statistical concepts in plain language. Share pvalues and confidence intervals, but also explain what they mean in terms of business outcomes. Visualization can also help make your data and insights more engaging and memorable.
By following these best practices, you‘ll be well on your way to leveraging statistical significance for maximum impact and minimum error in your marketing efforts. Of course, this is a complex topic, and there will always be more to learn. But with a commitment to rigorous methodology and continuous improvement, you‘ll set yourself apart as a datadriven marketing leader.
Helpful Resources for Marketers
 Neil Patel‘s A/B Testing Calculator
 Optimizely‘s Sample Size Calculator
 Evan Miller‘s ChiSquare Test calculator
 Backlinko‘s A/B Testing Guide
 HubSpot‘s A/B Testing Kit
 Harvard Business Review – A Refresher on A/B Testing
 Google Analytics Statistical Significance Calculator
With the knowledge and resources covered in this guide, you‘ll be equipped to make statistical significance a powerful asset in your datadriven marketing strategy. By understanding what it really means, how to calculate it, and how to apply it in your work, you can make better decisions, design smarter tests, and ultimately drive greater business results. Here‘s to significant marketing success!