Conducting a stat sig test: accurate experiment results

Ever wondered why some experiment results are taken seriously while others aren't? It's all about statistical significance. Whether you're tweaking your website's design or launching a new feature, understanding statistical significance can make the difference between a game-changing improvement and a costly mistake.

In this blog, we'll dive into the essentials of statistical significance in experiments. We'll break down complex concepts into everyday language, clear up common misconceptions, and share best practices to help you get accurate results. So let's get started!

Understanding statistical significance in experiments

is key when we're trying to make sense of experiment results. It helps us figure out whether the effects we see are real or just flukes. To get started, we need to set up some hypotheses. The null hypothesis assumes there's no effect, while the alternative hypothesis suggests there's something going on.

We also need to decide on a significance level (usually called alpha, α). This represents the risk we're willing to take of making a Type I error—that's when we think we've found something when we haven't. Common alpha values are 0.05 or 0.01, which help balance the risks of false positives and negatives. Then we calculate the , which tells us the probability of seeing results as extreme as ours if the null hypothesis is true.

But watch out for Type I and Type II errors! A Type I error is when we wrongly reject a true null hypothesis, and a Type II error is when we fail to reject a false one. Balancing these risks is super important when designing effective . We need to think carefully about the consequences of each type of error in our particular situation.

are tools that help us decide whether the differences we observe are likely due to chance or if there's a real effect. We calculate a test statistic and compare it to a critical value based on our chosen significance level. If our test statistic beats the critical value, we reject the null hypothesis and say our result is statistically significant.

Steps to accurately conduct a statistical significance test

Ready to run a stat sig test? Here's how to do it right.

First up, formulate clear hypotheses. That means spelling out your research hypothesis (what you think is happening) and your null hypothesis (the idea that nothing's going on). Picking the right statistical test is crucial—it depends on your data type and what you're testing for.

Some common tests are the Chi Square test for categorical data and T-tests for comparing means (check out this resource for more details). Don't forget to set your significance level (alpha), usually 0.05 or 0.01, which is the risk you're okay with for making a Type I error.

Next, calculate your test statistic and compare it to the critical value. This helps you decide whether to reject the null hypothesis. You'll also get a p-value, which tells you the probability of getting your results if the null hypothesis is true.

If your p-value is less than alpha, you might think "Eureka!" But hold on—statistical significance doesn't always mean practical significance. It's important to consider the effect size and what it means in the real world.

Finally, interpreting your results is about more than numbers. A low p-value suggests rejecting the null hypothesis, but you need to communicate your findings clearly. Use visuals and simple explanations to share the significance and implications of your results.

Common misconceptions and pitfalls in hypothesis testing

Even the pros can trip up on some common misconceptions in hypothesis testing.

First off, there's a lot of confusion about p-values. Remember, a p-value doesn't tell you the probability that your hypothesis is true or false. Instead, it tells you the likelihood of seeing your data if the null hypothesis is true. Misinterpreting p-values can seriously mess up your conclusions.

Then there's the infamous "peeking problem" in sequential testing. If you keep checking your results while the experiment is running, you increase the chance of a false positive. This happens because continuously monitoring metrics boosts the odds of finding a significant result just by chance. To avoid this, stick to predefined sample sizes and analysis plans, or use proper sequential testing methods.

Don't forget about Type I and Type II errors! As we mentioned before, balancing these risks is crucial. Proper test design and power analysis can help ensure reliable results.

Lastly, be careful with multiple comparisons. Testing lots of hypotheses at once increases the chances of false positives. Make sure to apply corrections—like the Bonferroni correction—to keep your significance levels where they should be (learn more about correction methods).

At Statsig, we're well aware of these pitfalls and have designed our tools to help you navigate around them.

Best practices for achieving accurate experiment results

Want to get accurate results from your experiments? Here are some best practices to keep in mind.

First, always ensure data quality. Rigorous data collection and validation methods—like A/A testing and excluding outliers—help spot errors and make your results more reliable. (For more on this, check out this article.)

Second, conduct power analysis to figure out the right sample size. This balances the risks of Type I and Type II errors, making sure your experiment has enough power to detect real effects. Learn more about power analysis here.

Third, monitor your experiment effectively. Keep an eye on metrics, especially early on, so you can quickly spot any issues or unexpected effects. (See how Statsig handles sequential testing here.)

Also, consider these tips:

• Implement guardrail metrics to catch anomalies and avoid dismissing good ideas.

• Shuffle users across experiments to reduce carryover effects from reusing control and treatment groups. (More insights here.)

By following these best practices, you'll boost the accuracy and reliability of your stat sig tests. Good data quality, proper power analysis, and effective monitoring all help ensure your experiments provide trustworthy insights for confident decision-making.

Closing thoughts

Understanding statistical significance is crucial for making informed decisions based on your experiments. By following best practices and being aware of common pitfalls, you can ensure your results are reliable and actionable. If you're looking for tools to help you along the way, Statsig offers comprehensive solutions for running experiments and interpreting results.

Feel free to explore more resources on statistical testing and experiment design. Hope you found this helpful!