As a mathematical concept, statistical significance plays a key role in helping you differentiate true outcomes from random variability. Statistical significance measures the confidence that the outcome in an A/B or multivariate test did not occur by chance alone. 

Here’s another way to put it: in a statistically significant result, the probability is high that the winning variant in a test truly outperformed the other variants and didn’t “win” due to chance. 

Statistical significance doesn’t necessarily imply practical or clinical significance. It doesn’t measure the size of an effect or its importance, only the confidence that the effect is non-random. Statistical significance doesn’t “prove” anything. It only supports or debunks the “null hypothesis” (e.g., that no effect or difference exists).

Statistical significance comes into play often in marketing scenarios where you’re testing the impact of various elements like messaging, ad copy, images, and layout. For example, let’s say you want to improve the conversion rate of your ecommerce website. You notice visitors often add items to their shopping cart, but leave without completing a sale. 

You suspect the culprit is some confusion about where to put an offer or coupon code, so you conduct an A/B test that positions the prompt for the code on the first page of the shopping cart versus the last. Then you run a test to see if the different coupon code positioning led to varying conversion rates for each version. If one performs better than the other, and this is proven by implementing statistical significance formulas, then you’ve proven your hypothesis that the placement of the coupon code form was impacting conversions.

Statistical Significance Example

Imagine that a pharma company just launched a blockbuster treatment for migraines.

They claim the drug’s ability to reduce headache duration and intensity are nothing short of life changing.

But what does “significant” mean? Are the results they’re using based on random variability or can they be replicated?

To back up their claim, the company must perform a series of tests and collect a trove of data that proves statistical significance. This gives doctors, patients, and government bodies the confidence that the drug’s outcome can be replicated.

Whether you’re creating a drug does that reduces headache pain or you’re assessing the outcome of a new marketing message, establishing statistical significance is important for making informed business decisions and accurate marketing claims.

Why is Statistical Significance Important?

If you’re googling things like “statistical significance with A/B testing” then the chances are you’re making decisions about where to allocate your budget or how to tweak your marketing campaigns. Knowing which results are truly successful versus those that are mere flukes is crucial. This is the intel that allows you to make smart data-driven decisions about how to optimize campaigns and invest resources. 

Benefits of statistical significance:

• It provides reasonable confidence that the observed outcomes of a given test aren’t random.
• It ensures that your results can be replicated in future studies or experiments, indicating that the findings are consistent and reliable.
• It supports informed decisions by validating the effectiveness of different strategies or approaches.
• It helps you avoid false positives, ensuring that you’re drawing conclusions based on genuine effects.
• It plays a vital role in either supporting or debunking hypotheses by providing a mathematical basis for accepting or rejecting them.

When is Statistical Significance Used?

Statistical significance is typically used when you need to determine if an observed change in performance (or other element) occurred because of a specific action or is a result of chance.  For marketers and business leaders, statistical significance provides much-needed clarity when it comes to evaluating the success of campaigns, understanding customer behaviors, and making strategic decisions based on market research. You must be able to conduct hypothesis testing so you can discern genuine trends from random variations. 

Testing Hypotheses: 

Two key concepts in this context of hypothesis testing are the P Value and the Confidence Interval which can be defined as:

• P Value 

Quantifies how likely it is that an observed difference occurred by chance. A low P value means that an observed effect likely didn’t occur randomly (so it supports the hypothesis.)

• Confidence Interval

Represents a range within which the true effect size is expected to lie with a certain level of confidence (usually 95%). It’s a way of expressing uncertainty in an estimate or measurement. A wider interval suggests more uncertainty about the true effect size, while a narrower interval indicates more precise estimates.

How to Calculate Statistical Significance

Here are the steps you need to calculate statistical significance:

1. Define your hypotheses – Start with a null hypothesis (no effect) and an alternative hypothesis (there is an effect).
2. Set the significance level – This is your threshold for deciding if an effect is statistically significant and is typically 5% (0.05).
3. Choose test type – Decide if a one-tailed or two-tailed test is appropriate for your research question. These are types of statistical tests used to determine the significance of results. Here’s a simple way to understand them:
   • One-tailed test – You have a new fertilizer, and you want to prove it makes plants grow taller than usual. A one-tailed test would only look for an increase in height. It’s like saying, “I’m only checking if this fertilizer makes plants taller, not shorter.”
   • Two-tailed test – Suppose you’re wondering if the fertilizer affects height in any way (taller or shorter). A two-tailed test checks for both possibilities – whether plants grow taller or shorter. It’s like saying, “I want to see if this fertilizer makes any difference in height, regardless of the direction.”
4. Determine sample size – Use power analysis to estimate the required sample size for reliable results. A power analysis is a statistical method that helps you determine the right sample size required for a given experiment. Most testing software has built-in functions to help calculate the appropriate sample size.
5. Calculate standard deviation – Here’s a handy formula to help with this:
   • standard deviation = √((∑|x−μ|^ 2) / (N-1))
6. Perform the statistical test – Use an appropriate test (e.g., t-test) to compare your data against the null hypothesis.
7. Interpret the results – Compare the p-value from your test with your significance level to decide if your results are statistically significant.