How to determine if your results are statistically significant

Ever stared at a batch of data and wondered if the trends you're spotting are genuine or just random flukes? We've all been there. Understanding statistical significance might seem like cracking a complex code, but it's key to making informed, data-driven decisions.

In this blog, we'll demystify statistical significance, p-values, and significance levels. We'll walk through essential steps, highlight common mistakes to avoid, and help you confidently interpret your results. Let's dive in!

Grasping the basics of statistical significance

Statistical significance is all about figuring out whether the results we're seeing are real or just random luck. It helps us decide if a difference or effect we've observed actually means something, or if it's just noise. Understanding statistical significance is crucial for making smart decisions based on data.

Ever heard of p-values? They're like a reality check. A p-value tells us how likely it is to get the results we did, assuming there's no real effect (that's the null hypothesis). A low p-value suggests our results probably didn't happen by chance. But watch out—p-values can be tricky to interpret, so it's important to understand their limitations and the context. For more insights, check out this overview on interpreting p-values.

The significance level, usually set at 0.05, is like our cutoff point for saying "Hey, this result is significant!" If our p-value is below this threshold, we consider the results statistically significant. Choosing the right significance level depends on the consequences of making errors—like false positives (Type I errors) or false negatives (Type II errors). You can read more about it here.

To get trustworthy results, we need to follow proper steps and use sound experimentation methods. Experimentation frameworks help us validate our systems and catch any errors along the way. By using techniques like A/A tests, excluding outliers, and preventing carryover effects, we can keep our data clean. And remember: interpreting results isn't just about the numbers—we need to think about what they mean in the real world, keeping our study's goals in mind.

The essential steps to test for statistical significance

So, how do you actually test for statistical significance? Here's a simple roadmap:

First, clearly define your null and alternative hypotheses. The null hypothesis assumes there's no effect or difference, while the alternative says there is.

Next up, choose the right statistical test based on your data and what you're trying to find out. And make sure you have enough data to make your test powerful—you can use a sample size calculator to figure out how many samples you need.

Don't forget to collect unbiased data that truly represents your population. Make sure your data collection methods avoid any errors that could mess with your results.

Once you have your data, run the statistical test and calculate the p-value. If the p-value is below your chosen significance level (usually 0.05), you can reject the null hypothesis—meaning your results are statistically significant.

Finally, interpret your findings in the context of your experiment and business goals. Remember, statistical significance doesn't always mean practical importance. It's wise to replicate and retest to confirm your conclusions.

Tools like Statsig can help streamline this entire process, from setting up experiments to interpreting the results effectively.

Avoiding common mistakes when determining significance

Even with the best intentions, it's easy to stumble when determining statistical significance. Let's look at some common pitfalls and how to dodge them.

First off, choosing the wrong statistical test can lead to misleading results. For instance, the Mann-Whitney U test is often misused, which can mess up interpretations about mean differences. To get valid results, make sure the test matches your data and hypothesis.

Another biggie is misinterpreting p-values. A low p-value suggests your results are unlikely due to chance, but it doesn't automatically mean they're practically significant. When you look at p-value histograms, watch out for unusual patterns like bimodal or sparse distributions—they might hint at problems with your tests or data.

Also, beware of selection bias and p-hacking. These practices can lead to false positives or negatives, steering you in the wrong direction. To avoid these traps:

• Define your hypothesis before collecting data.

• Use a sample size calculator to ensure you have enough power.

• Resist the urge to cherry-pick data or tweak tests just to achieve significance.

By keeping an eye out for these common mistakes, you'll be better equipped to determine if your results are truly statistically significant. Platforms like Statsig can help you set up experiments correctly and reduce the risk of these common errors.

Interpreting and acting on your statistical results

So you've got your results—now what? It's time to make sense of them and decide your next steps.

First, distinguish between statistical significance and practical relevance. Just because a result is statistically significant doesn't mean it's meaningful for your business. Consider the effect size and how it might impact your goals.

Next, align your findings with your initial objectives. Use the insights to inform your decision-making process. If the results are both statistically significant and practically relevant, you might decide to implement changes based on your experiment. If not, it might be time to tweak your experiment or try a different approach.

When sharing your findings with others, be clear and transparent about the results, including any limitations or assumptions. Use visuals and simple language to help everyone understand what the results mean. By providing an honest assessment, you enable better decision-making and build trust in the process.

Remember, statistical significance alone doesn't guarantee success. Keep monitoring the impact of your decisions and be ready to adapt as needed. Embracing a culture of experimentation and data-driven decisions can help you continuously improve your products and services.

Consider using tools like Statsig to help you interpret your results effectively and make informed decisions.

Closing thoughts

Grasping statistical significance doesn't have to be daunting. With a solid understanding of the basics, careful attention to potential pitfalls, and thoughtful interpretation of your results, you're well on your way to making smarter, data-driven decisions.

To dive deeper into these topics, check out the resources linked throughout this post. And don't hesitate to explore services like Statsig, which can help you run experiments effectively and make sense of your data.

Hope you found this helpful!