Statistical Significance

Statistical significance is a term used in hypothesis testing to refer to the likelihood that the relationship between two or more variables is caused by something other than chance. Statistical hypothesis testing is a method of making decisions using data, whether from a controlled experiment or an observational study (not controlled).

In hypothesis testing, a p-value is used to determine statistical significance. The p-value measures the probability of the metric lift you observe (or a more extreme lift) assuming that the variant you’re testing has no effect. The standard is to use a p-value less than 0.05 to identify variants that have a statistically significant effect. A p-value less than 0.05 implies that there’s less than 5% chance of seeing the observed metric lift (or a more extreme metric lift) if the variant had no effect. In practice, a p-value that's lower than your pre-defined threshold is treated as evidence for there being a true effect.

Another approach to assess statistical significance is using a confidence interval. A confidence interval examines whether the metric difference between the variant and control overlaps with zero. A 95% confidence interval is the range that covers the true difference 95% of the time. It is usually centered around the observed delta between the variant and control.

For example, if you are testing the effect of a new website design on the amount of time visitors spend on your website, you would compare the time spent by visitors who saw the new design (the treatment group) to the time spent by visitors who saw the old design (the control group). If the p-value is less than 0.05, you might conclude that the new design leads to a statistically significant increase in time spent on the website.

However, it's important to note that statistical significance does not always imply practical significance. A result might be statistically significant but the actual difference between the groups might be so small that it doesn't have any practical implications.

Also, when running multiple tests, the chance of observing a statistically significant result just by chance increases. This is known as the problem of multiple comparisons, and it is often addressed by adjusting the significance level using methods like the Bonferroni correction. The Bonferroni correction reduces the probability of Type I errors (false positives) by adjusting the significance level (α). The significance level is divided by the number of comparisons (equivalent to the number of test variants) in the experiment.

Loved by customers at every stage of growth

See what our users have to say about building with Statsig
OpenAI
"At OpenAI, we want to iterate as fast as possible. Statsig enables us to grow, scale, and learn efficiently. Integrating experimentation with product analytics and feature flagging has been crucial for quickly understanding and addressing our users' top priorities."
Dave Cummings
Engineering Manager, ChatGPT
SoundCloud
"We evaluated Optimizely, LaunchDarkly, Split, and Eppo, but ultimately selected Statsig due to its comprehensive end-to-end integration. We wanted a complete solution rather than a partial one, including everything from the stats engine to data ingestion."
Don Browning
SVP, Data & Platform Engineering
Recroom
"Statsig has been a game changer for how we combine product development and A/B testing. It's made it a breeze to implement experiments with complex targeting logic and feel confident that we're getting back trusted results. It's the first commercially available A/B testing tool that feels like it was built by people who really get product experimentation."
Joel Witten
Head of Data
"We knew upon seeing Statsig's user interface that it was something a lot of teams could use."
Laura Spencer
Chief of Staff
"The beauty is that Statsig allows us to both run experiments, but also track the impact of feature releases."
Evelina Achilli
Product Growth Manager
"Statsig is my most recommended product for PMs."
Erez Naveh
VP of Product
"Statsig helps us identify where we can have the most impact and quickly iterate on those areas."
John Lahr
Growth Product Manager
"The ability to easily slice test results by different dimensions has enabled Product Managers to self-serve and uncover valuable insights."
Preethi Ramani
Chief Product Officer
"We decreased our average time to decision made for A/B tests by 7 days compared to our in-house platform."
Berengere Pohr
Team Lead - Experimentation
"Statsig is a powerful tool for experimentation that helped us go from 0 to 1."
Brooks Taylor
Data Science Lead
"We've processed over a billion events in the past year and gained amazing insights about our users using Statsig's analytics."
Ahmed Muneeb
Co-founder & CTO
SoundCloud
"Leveraging experimentation with Statsig helped us reach profitability for the first time in our 16-year history."
Zachary Zaranka
Director of Product
"Statsig enabled us to test our ideas rather than rely on guesswork. This unlocked new learnings and wins for the team."
David Sepulveda
Head of Data
Brex
"Brex's mission is to help businesses move fast. Statsig is now helping our engineers move fast. It has been a game changer to automate the manual lift typical to running experiments and has helped product teams ship the right features to their users quickly."
Karandeep Anand
President
Ancestry
"We only had so many analysts. Statsig provided the necessary tools to remove the bottleneck. I know that we are able to impact our key business metrics in a positive way with Statsig. We are definitely heading in the right direction with Statsig."
Partha Sarathi
Director of Engineering
"Statsig has enabled us to quickly understand the impact of the features we ship."
Shannon Priem
Lead PM
Ancestry
"I know that we are able to impact our key business metrics in a positive way with Statsig. We are definitely heading in the right direction with Statsig."
Partha Sarathi
Director of Engineering
"Working with the Statsig team feels like we're working with a team within our own company."
Jeff To
Engineering Manager
"[Statsig] enables shipping software 10x faster, each feature can be in production from day 0 and no big bang releases are needed."
Matteo Hertel
Founder
"We use Statsig's analytics to bring rigor to the decision-making process across every team at Wizehire."
Nick Carneiro
CTO
Notion
"We've successfully launched over 600 features behind Statsig feature flags, enabling us to ship at an impressive pace with confidence."
Wendy Jiao
Staff Software Engineer
"We chose Statsig because it offers a complete solution, from basic gradual rollouts to advanced experimentation techniques."
Carlos Augusto Zorrilla
Product Analytics Lead
"We have around 25 dashboards that have been built in Statsig, with about a third being built by non-technical stakeholders."
Alessio Maffeis
Engineering Manager
"Statsig beats any other tool in the market. Experimentation serves as the gateway to gaining a deeper understanding of our customers."
Toney Wen
Co-founder & CTO
"We finally had a tool we could rely on, and which enabled us to gather data intelligently."
Michael Koch
Engineering Manager
Notion
"At Notion, we're continuously learning what our users value and want every team to run experiments to learn more. It's also critical to maintain speed as a habit. Statsig's experimentation platform enables both this speed and learning for us."
Mengying Li
Data Science Manager
Whatnot
"Excited to bring Statsig to Whatnot! We finally found a product that moves just as fast as we do and have been super impressed with how closely our teams collaborate."
Rami Khalaf
Product Engineering Manager
"We realized that Statsig was investing in the right areas that will benefit us in the long-term."
Omar Guenena
Engineering Manager
"Having a dedicated Slack channel and support was really helpful for ramping up quickly."
Michael Sheldon
Head of Data
"Statsig takes away all the pre-work of doing experiments. It's really easy to setup, also it does all the analysis."
Elaine Tiburske
Data Scientist
"We thought we didn't have the resources for an A/B testing framework, but Statsig made it achievable for a small team."
Paul Frazee
CTO
Whatnot
"With Warehouse Native, we add things on the fly, so if you mess up something during set up, there aren't any consequences."
Jared Bauman
Engineering Manager - Core ML
"In my decades of experience working with vendors, Statsig is one of the best."
Laura Spencer
Technical Program Manager
"Statsig is a one-stop shop for product, engineering, and data teams to come together."
Duncan Wang
Manager - Data Analytics & Experimentation
Whatnot
"Engineers started to realize: I can measure the magnitude of change in user behavior that happened because of something I did!"
Todd Rudak
Director, Data Science & Product Analytics
"For every feature we launch, Statsig saves us about 3-5 days of extra work."
Rafael Blay
Data Scientist
"I appreciate how easy it is to set up experiments and have all our business metrics in one place."
Paulo Mann
Senior Product Manager
We use cookies to ensure you get the best experience on our website.
Privacy Policy