Statistical vs. practical significance: why both matter in experiments

Ever run an experiment and wondered if the results actually matter? We've all been there—staring at p-values and trying to make sense of them. Statistical significance might tell us something's happening, but does it really make a difference in the real world?

In this blog, we'll dive into the world of statistical and practical significance, exploring how they differ and why both are essential. Let's uncover how to interpret experimental results in a way that truly informs our decisions.

Exploring statistical significance in experimental results

Statistical significance helps us figure out if our experimental results are just a fluke or if there's something real happening. We usually look at p-values for this, which tell us the probability of seeing our results if the null hypothesis is true. The lower the p-value, the less likely our results are due to chance.

But what's considered a "low" p-value? Usually, we deem results statistically significant if the p-value is below 0.05 or 0.01. But here's the thing: sample size also plays a big role. With a large sample, even tiny effects can become statistically significant. So, it's important not just to look at statistical significance but also to think about practical significance when we interpret findings.

Focusing only on statistical significance can be misleading. A result might be statistically significant but not practically meaningful—especially if you have a huge sample size. On the flip side, not finding statistical significance doesn't necessarily mean there's no effect, particularly if your sample size is small or there's a lot of variability.

So how do we assess if our statistically significant results matter in the real world? That's where effect size and practical significance come in. Effect size tells you how big the difference is between groups or how strong the relationship is between variables. Practical significance asks: is this effect big enough to matter in the real world?

When sharing your experimental results, it's a good idea to include both statistical significance and effect size. Confidence intervals can also help show how precise your estimates are and the range of plausible values. At Statsig, we emphasize providing a comprehensive picture of your findings to help you make informed decisions.

Unveiling practical significance: assessing real-world impact

Sure, statistical significance tells us that an effect exists, but practical significance asks if that effect really matters in the real world. Effect sizes help us gauge how big that effect actually is. For instance, imagine a statistically significant 2% bump in website conversions—does that really justify the cost and effort to make a change?

Sometimes, statistically significant results don't have much practical value. Suppose a study shows a significant difference in exam scores between two teaching methods, but the difference is only a couple of points. Is that enough to overhaul the entire education system? Probably not. Similarly, a medical trial might find a statistically significant improvement in patient outcomes, but if the effect is tiny, it might not be worth the risks or costs of the new treatment.

To figure out practical significance, we need to think about the context and implications of the results. In online experiments—like those you might run with Statsig—even small effect sizes can be meaningful because we can scale improvements across lots of users. But in fields like psychology or social sciences, we might need larger effect sizes to show practical significance.

Bottom line: when interpreting results, it's important to consider both statistical and practical significance. A low p-value suggests there's likely a real effect, but the effect size tells us if that difference is big enough to matter in the real world. By understanding how statistical and practical significance relate, we can make smarter decisions based on the data.

Bridging the gap between statistical and practical significance

Statistical significance and practical significance are both key when it comes to making informed decisions from experiments. While statistical significance tells us how likely it is that our results aren't just due to chance, practical significance looks at the real-world impact of those results. Confidence intervals can help bridge the gap by giving us a range of possible effect sizes.

If we ignore practical significance and only focus on statistical measures, we might end up making bad calls. For example, a statistically significant result with a tiny effect size might not be worth changing your business practices over. Conversely, a result that's not statistically significant but has a large effect size could still be important.

So, to bring together statistical and practical significance effectively, here are some things to consider:

• Figure out the smallest effect size that's meaningful in your context.

• Use confidence intervals to estimate the range of possible effect sizes.

• Interpret results considering both statistical significance and practical importance.

• Communicate your findings clearly, highlighting the real-world implications.

By taking this well-rounded approach that looks at both statistical and practical significance—and using platforms like Statsig to analyze your results—you can make more informed and impactful decisions based on your experiments. This way, you're focusing on the insights that matter most, ultimately driving better outcomes for your business or research.

Implementing best practices in experimental analysis

Bringing together your statistical findings with your domain expertise is key to getting actionable insights. When you consider practical implications alongside statistical significance, you make better decisions that lead to real results. This way, your findings aren't just statistically valid—they're also relevant to what you're actually trying to achieve.

To keep your experiments trustworthy, it's important to avoid pitfalls like p-hacking and misreading p-values. P-hacking happens when you tweak your data or analysis methods just to get statistically significant results, leading to false positives and unreliable conclusions. By sticking to solid statistical practices and maybe even pre-registering your analysis plan, you can reduce the risks of p-hacking and make sure your findings are credible.

Communicating your results effectively is crucial, too. When you share your findings, try to balance statistical significance with practical significance. Use clear and concise language to explain statistical concepts and what they mean in the real world. Visual aids like graphs and charts can help show the size and direction of the effects you've found. By adapting your communication to your audience's level of expertise, you make sure your insights are understood and can be acted upon.

Adopting best practices in experimental analysis means committing to continuous learning and improvement. Stay up-to-date with the latest in statistical methods and experimentation techniques. Engage with the data science community through blogs, forums, and conferences to share ideas and learn from others. By fostering a culture of experimentation and data-driven decisions, you can unlock the full potential of your experiments and make a real impact in your organization.

Closing thoughts

Understanding both statistical significance and practical significance is key to making smart decisions from your experiments. By considering not just whether an effect exists, but also if it has real-world impact, you focus on what truly matters. Don't forget to look at effect sizes, use confidence intervals, and communicate your findings in a way that's clear and actionable.

If you want to dive deeper, consider exploring more resources or check out tools like Statsig that support effective experimental analysis. Hope you found this helpful and happy experimenting!