{"id":111854,"date":"2021-06-21T12:20:31","date_gmt":"2021-06-21T12:20:31","guid":{"rendered":"https:\/\/staging.wingify.com\/blog\/statistical-significance\/"},"modified":"2026-09-04T15:18:28","modified_gmt":"2026-09-04T09:48:28","slug":"statistical-significance","status":"publish","type":"post","link":"https:\/\/wingify.com\/blog\/statistical-significance\/","title":{"rendered":"The Role of Statistical Significance in A\/B Testing"},"content":{"rendered":"\n

Statistical significance is a powerful yet often underutilized digital marketing tool. <\/strong><\/p>\n\n\n\n

A concept that is theoretical and practical in equal measures, you can use statistical significance models to optimize many of your business\u2019s core marketing activities (A\/B testing included).<\/p>\n\n\n\n

A\/B testing is integral to improving the user experience (UX) of a consumer-facing touchpoint (a landing page, checkout process, mobile application, etc.) and increasing its performance while encouraging conversions.<\/p>\n\n\n\n

By creating two versions of a particular marketing asset, both with slightly different functions or elements, and analyzing their performance, it\u2019s possible to develop an optimized landing page, email, web app, etc. that yields the best results. This methodology is also referred to as two-sample hypothesis testing.<\/p>\n\n\n\n

When it comes to success in A\/B testing, statistical significance plays an important role. In this article, we will explore the concept in more detail and consider how statistical significance can enhance the A\/B testing process<\/a>.<\/p>\n\n\n\n

But before we do that, let\u2019s look at the meaning of statistical significance.<\/p>\n\n\n\n

What is statistical significance and why does it matter?<\/h2>\n\n\n\n

According to Investopedia,<\/a> statistical significance is defined as:<\/p>\n\n\n\n

\u201cThe claim that a result from data generated by testing or experimentation is not likely to occur randomly or by chance but is instead likely to be attributable to a specific cause.”<\/p>\n\n\n\n

In that sense, statistical significance will bestow you with the tools to drill down into a specific cause, thereby making informed decisions that are likely to benefit the business. In essence, it\u2019s the opposite of shooting in the dark.<\/p>\n\n\n

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\"Statistical
Make informed decisions with testing and experimentation<\/figcaption><\/figure>\n<\/div>\n\n\n

Calculating statistical significance<\/h2>\n\n\n\n

To calculate statistical significance accurately, most people use Pearson\u2019s chi-squared test or distribution.<\/p>\n\n\n\n

Invented by Karl Pearson<\/a>, the chi (which represents \u2018x\u2019 in Greek)-squared test commands that users square their data to highlight possible variables.<\/p>\n\n\n\n

This methodology is based on whole numbers. For instance, chi-squared is often used to test marketing conversions\u2014a clear-cut scenario where users either take the desired action or they don\u2019t.<\/p>\n\n\n\n

In a digital marketing context, people apply Pearson\u2019s chi-squared method using the following formula:<\/p>\n\n\n\n

Statistically significant = Probability (p) < Threshold (\u0251)<\/em><\/p>\n\n\n\n

Based on this notion, a test or experiment is viewed as statistically significant if the probability (p) turns out lower than the appointed threshold (a), also referred to as the alpha. In plainer terms, a test will prove statistically significant if there is a low probability that a result has happened by chance.<\/p>\n\n\n\n

Statistical significance is important because applying it to your marketing efforts will give you confidence that the adjustments you make to a campaign, website, or application will have a positive impact on engagement, conversion rates, and other key metrics.<\/p>\n\n\n\n

Essentially, statistically significant results aren\u2019t based on chance and depend on two primary variables: sample size<\/a> and effect size<\/a>.<\/p>\n\n\n\n

Statistical significance and digital marketing<\/h2>\n\n\n\n

At this point, it\u2019s likely that you have a grasp of the role that statistical significance plays in digital marketing.<\/p>\n\n\n\n

Without validating your data or giving your discoveries credibility, you will probably have to take promotional actions that offer very little value or return on investment (ROI), particularly when it comes to A\/B testing.<\/p>\n\n\n\n

Despite the wealth of data available in the digital age, many marketers are still making decisions based on their gut<\/a>.<\/p>\n\n\n\n

While the shooting in the dim light approach may yield positive results on occasion, to create campaigns or assets that resonate with your audience on a meaningful level, making intelligent decisions based on watertight insights is crucial.<\/p>\n\n\n\n

That said, when conducting tests or experiments based on key elements of your digital marketing activities, taking a methodical approach will ensure that every move you make offers genuine value, and statistical significance will help you do so.<\/p>\n\n\n\n

Using statistical significance for A\/B testing<\/h2>\n\n\n\n

Now we move on to A\/B testing, or more specifically, how you can use statistical significance techniques to enhance your A\/B testing efforts.<\/p>\n\n\n\n

Testing uses<\/strong><\/p>\n\n\n\n

Before we consider its practical applications, let\u2019s consider what A\/B tests you can run using statistical significance:<\/p>\n\n\n\n

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  • Emails clicks, open rates, and engagements<\/li>\n\n\n\n
  • Landing page conversion rates<\/li>\n\n\n\n
  • Notification responses<\/li>\n\n\n\n
  • Push notification conversions<\/li>\n\n\n\n
  • Customer reactions and browsing behaviors<\/li>\n\n\n\n
  • Product launch reactions<\/li>\n\n\n\n
  • Website calls to action (CTAs)<\/li>\n<\/ul>\n\n\n\n

    The statistical steps<\/h2>\n\n\n\n

    To conduct successful A\/B tests using statistical significance (the chi-squared test), you should follow these definitive steps:<\/p>\n\n\n\n

    1. Set a null hypothesis<\/h3>\n\n\n\n

    The idea of the null hypothesis is that it won\u2019t return any significant results. For example, a null hypothesis might be that there is no affirmative evidence to suggest that your audience prefers your new checkout journey to the original checkout journey. Such a hypothesis or statement will be used as an anchor or a benchmark.<\/p>\n\n\n\n

    2. Create an alternative theory or hypothesis<\/h3>\n\n\n\n

    Once you\u2019ve set your null hypothesis, you should create an alternative theory, one that you\u2019re looking to prove, definitively. In this context, the alternative statement could be: our audience does favor our new checkout journey.<\/p>\n\n\n\n

    3. Set your testing threshold<\/h3>\n\n\n\n

    With your hypotheses in place, you should set a percentage threshold (the (a) or alpha) that will dictate the validity of your theory. The lower you set the threshold\u2014or (a)\u2014the stricter the test will be. If your test is based on a wider asset such as an entire landing page, then you might set a higher threshold than if you\u2019re analyzing a very specific metric or element like a CTA button, for instance.<\/p>\n\n\n\n

    For conclusive results, it\u2019s imperative to set your threshold prior to running your A\/B test or experiment.<\/p>\n\n\n\n

    4. Run your A\/B test<\/h3>\n\n\n\n

    With your theories and threshold in place, it\u2019s time to run the A\/B test. In this example, you would run two versions (A and B) of your checkout journey and document the results.<\/p>\n\n\n\n

    Here you might compare cart abandonment and conversion rates to see which version has performed better. If checkout journey B (the newer version) has outperformed the original (version A), then your alternative theory or hypothesis will be proved correct.<\/p>\n\n\n\n

    5. Apply the chi-squared method<\/h3>\n\n\n\n

    Armed with your discoveries, you will be able to apply the chi-squared test to determine whether the actual results differ from the expected results.<\/p>\n\n\n\n

    To help you apply chi-squared calculations to your A\/B test results, here\u2019s a video tutorial for your reference:<\/p>\n\n\n\n

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