Statistical significance, or “stat sig,” is a calculation that shows whether your test results are likely due to real changes in user behavior or random chance. In analytics and experimentation, this means checking if a test result is reliable enough to act on.

Using stat sig correctly can help your team confidently create impactful strategies, reduce risk, and drive meaningful outcomes.

What is statistical significance?

Statistical significance measures the likelihood that your A/B test results are not due to chance. In other words, it helps you determine if differences in your test results are real or just random fluctuations.

Statistical significance is based on a starting assumption that there’s no real difference between the groups being tested. This assumption is called the null hypothesis. If your experiment shows a statistically significant result, it means you have enough evidence to reject the null hypothesis and conclude that there is a significant difference between the groups, and it’s most likely not due to chance.

If the result isn’t statistically significant, you can’t confidently say there’s a real difference, so the null hypothesis remains in place.

There are three terms to remember to help you understand what your statistical significance calculations mean:

• Control: The group or version in an experiment that doesn’t receive any changes; it serves as the baseline against which to compare.
• Variant: The group or version in an experiment where a change or new feature is introduced. The variant is tested against the control to measure its impact.
• P-value: A number that shows how likely it is that the results from your experiment happened by chance. A low p-value (typically less than 0.05) means the result is likely real, not random.

Why is statistical significance important?

Statistical significance helps teams avoid making decisions based on random or misleading data. For example, without stat sig, a team might mistakenly believe that a slight increase in the conversion rate after a website update is meaningful when, in reality, it could be due to other external factors (like seasonality) or internal factors (like misreading the data).

Stat sig also gives teams the confidence to act on data-driven insights with certainty. It can even guide more significant strategic decisions by providing reliable data.

Without stat sig, teams might make decisions based on fluke data (or no data), increasing the likelihood of making risky decisions. However, with stat sig, you’re less likely to waste resources on ideas that won’t deliver consistent results over time.

When to use a statistical significance calculator

Statistical significance is most useful when running tests or experiments and determining whether the results are meaningful. A few everyday use cases where stat sig can help you make strategic decisions are A/B testing or split testing, product or feature testing, funnel analysis, and market research.

A/B testing

Many teams use A/B testing for web pages, email marketing campaigns, social media posts, and ads. Stat sig in A/B testing is crucial when tracking metrics and strategizing for specific goals or outcomes, like higher click-through rates or conversion rates. For instance, an ecommerce company might test two versions of a product page to see which leads to more purchases.

Product or feature testing

In product or feature testing, teams use statistical significance to evaluate changes like new interface designs, additional functionality, or updated workflows. This ensures that any updates improve user experience and engagement before rolling them out widely. For example, a finance app might test a new dashboard or a new feature like push notifications to see if it improves retention.

Funnel analysis

In funnel analysis, statistical significance helps teams determine if changes at specific stages, like sign-ups, product trials, or checkouts, are leading to consistent improvements in your customer journeys. It’s especially useful for identifying where users drop off and whether adjustments to the funnel could have a lasting impact on conversions, such as simplifying steps or improving messaging.

Market research

In market research, statistical significance helps your team validate survey results, customer feedback, or demographic studies. It helps analysts determine whether the patterns they observe—such as preferences for a new product feature or shifts in purchasing behavior—are genuine or random. Many teams use market research to guide product development, customer segmentation, and marketing strategies.

How to calculate statistical significance

To calculate statistical significance:

1. Define your null hypothesis, which states no difference between the groups. For example, imagine a company testing two landing page versions to see which generates more leads. The null hypothesis would be that both versions will yield the same number of leads.
2. Collect your data set, including sample size, mean, and standard deviation for each group.
3. Calculate your test statistic—such as a t-test for mean, a chi-square test for proportions, or a z-score to measure how far a result deviates from the mean—to measure the difference between the groups.
4. Determine your p-value, which shows the probability that the observed difference happened by chance, and compare it to your confidence level—typically 95%—to assess statistical significance. If the p-value is below a predetermined threshold (commonly 0.05), you can reject the null hypothesis and conclude that the difference is statistically significant. For example, if you’re testing two versions of a website homepage, a p-value below 0.05 indicates less than a 5% chance that any observed difference in the number of visitors between the two pages happened randomly.

How to use our statistical significance calculator

Amplitude’s statistical significance calculator is simple and user-friendly. It enables teams to input their data quickly and clearly understand their test results. Here’s how to use it:

1. Enter the number of conversions and visitors for Test A, then repeat with the numbers for Test B. Select “Calculate.”

2. Review the results and Amplitude’s explanation of them.

For teams that need deeper insights and more robust analytics, Amplitude’s full platform offers advanced stat sig features like sequential testing and A/B testing across different charts to explore user behavior in more detail. You can adjust parameters like sample size and p-values to fit specific testing needs, which results in more precise experimentation and reliable conclusions​.

Common statistical significance mistakes

Making mistakes in stat sig can lead to misguided decisions that don’t result in improvements. Avoid these common mistakes so you can more accurately analyze your results.

Overlooking sample size requirements

Stat sig calculations heavily rely on having a sample size that is large enough. If the sample is too small, your results may not be trustworthy, and you may end up with false negatives. Many analytics platforms consider this for teams looking to calculate stat sig. Amplitude, for example, sets a minimum sample size requirement to help avoid this issue.

There’s also the concept of sample ratio mismatch (SRM), which occurs when your test’s control and variant groups don’t have equal participants. This imbalance can lead to skewed or unreliable results. When both groups are properly balanced, it helps maintain the integrity of your test and ensures your conclusions are accurate.

Unsure what sample size to use? Amplitude has a sample size calculator you can use to calculate your ideal sample size.

Forgetting to consider external factors

Statistically significant differences don’t always mean that your change caused the outcome. In experimentation, teams consider other factors that could’ve caused or contributed to the change. For instance, external factors––like timing (e.g., a holiday) or a major marketing campaign by a competitor running simultaneously––could impact the results.

Running too many tests

You’re more likely to get false positives when running many tests simultaneously. The “multiple comparison problem” increases the chance of finding a statistically significant result by chance alone. If you’re testing multiple variants or analyzing several user behaviors, each test adds a 5% chance of showing a false positive, leading to misleading conclusions. To address this, your teams can apply adjustments, like Bonferroni corrections, which can help you maintain the integrity of your results.

Misinterpreting the p-value

Many people mistakenly think that a p-value below 0.05 means a 95% chance that the result is accurate, which is an incorrect interpretation. A p-value below the significance level, typically 0.05, just means there’s less than a 5% chance your result happened by random chance and rather more likely that it happened because of a change you made.

Misunderstanding the p-value can lead to overconfidence in your findings. For example, if your team believes a p-value can guarantee the effectiveness of a feature, you might push it live without considering other factors like data quality, sample size, or external factors. Overconfidence could result in changes that don’t drive value, and your team might miss out on deeper insights by not examining the full context of the experiment.

Turn insights into action with statistical significance

Stat sig helps you cut through the noise and provides clear, actionable insights for your experiments. By understanding and applying stat sig, you’ll make confident, data-driven decisions that impact your business.