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Thoughts and insights from the team at Statsig
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107
How Google's June outage could have been fixed with a feature flag
The Statsig Team
Sat Jun 28 2025
111
Un guide rapide sur la disparité du ratio d'échantillonnage (SRM)
The Statsig Team
Tue Jun 24 2025
145
Contrôler vos erreurs de type I : Bonferroni et Benjamini-Hochberg
The Statsig Team
Tue Jun 24 2025
152
Una guía rápida sobre la discrepancia en la proporción de la muestra (SRM)
The Statsig Team
Tue Jun 24 2025
370
MDE in A/B testing: Setting realistic expectations for your experiments
The Statsig Team
Mon Jun 23 2025
371
The multiple comparisons problem: Why running many A/B tests requires special care
The Statsig Team
Mon Jun 23 2025
372
Sequential testing: How to peek at A/B test results without ruining validity
The Statsig Team
Mon Jun 23 2025
373
Confidence intervals in A/B testing: Beyond simple win/loss metrics
The Statsig Team
Mon Jun 23 2025
378
CTR optimization: A/B testing your way to higher click-through rates
The Statsig Team
Mon Jun 23 2025
379
A/B testing revenue metrics: Optimizing for long-term business value
The Statsig Team
Mon Jun 23 2025
381
Establishing baseline metrics: The starting point for every A/B test
The Statsig Team
Mon Jun 23 2025
382
Chi‑square versus t‑test: when and how to use each statistical test
The Statsig Team
Wed Apr 16 2025
385
Product functionality versus benefits: how to communicate value
The Statsig Team
Sun Apr 13 2025
409
Pinpointing confounding variables to improve experiment accuracy
The Statsig Team
Tue Mar 18 2025
411
Introduction to Heap Analytics: automated event tracking explained
The Statsig Team
Sat Mar 15 2025
413
Export data definition: when and why to move information externally
The Statsig Team
Fri Mar 14 2025
436
P-values and significance levels: What they mean in hypothesis testing
The Statsig Team
Sun Mar 02 2025
437
LTV analysis: How to measure customer lifetime value accurately
The Statsig Team
Sun Mar 02 2025
438
How do I effectively share analytics insights with stakeholders?
The Statsig Team
Sat Mar 01 2025
444
Average e‑commerce conversion rate by industry (2025 benchmarks)
The Statsig Team
Wed Feb 26 2025
447
Confidence level vs. significance level: key differences explained
The Statsig Team
Sun Feb 23 2025
455
What is website analytics? How to track and improve site performance
The Statsig Team
Mon Feb 17 2025
456
Understanding two-tailed tests: when and why to use them in experiments
The Statsig Team
Mon Feb 17 2025
477
First-order effects in data analysis: What they mean and how to handle them
The Statsig Team
Wed Feb 12 2025
480
Click-through rate demystified: How to turn impressions into actions
The Statsig Team
Mon Feb 10 2025
496
Exponential growth basics: Strategies to skyrocket product adoption
The Statsig Team
Wed Feb 05 2025
504
One-tailed vs. two-tailed hypothesis: Key differences & when to use each
The Statsig Team
Mon Feb 03 2025
507
How to use the Statsig calculator for accurate experiment analysis
The Statsig Team
Mon Feb 03 2025
508
SQL data types explained: MySQL, Postgres & SQL Server cheat sheet
The Statsig Team
Mon Feb 03 2025
511
What’s the difference between quantitative and qualitative data?
The Statsig Team
Sun Feb 02 2025
515
Evaluating business analytics software for data-driven decisions
The Statsig Team
Sat Feb 01 2025
518
Deep customer analytics: How to extract insights from your data
The Statsig Team
Fri Jan 31 2025
527
ARR defined: Why annual recurring revenue drives sustainable growth
The Statsig Team
Tue Jan 28 2025
541
Confidence interval percentage: What it means in statistical analysis
The Statsig Team
Sat Jan 25 2025
548
Event tracking fundamentals: why it matters for product insights
The Statsig Team
Thu Jan 23 2025
552
Tracking events in experiments: best practices for accurate data
The Statsig Team
Wed Jan 22 2025
555
How descriptive analytics lays the foundation for deeper insights
The Statsig Team
Tue Jan 21 2025
557
Making data-backed decisions: A practical intro to business analytics
The Statsig Team
Tue Jan 21 2025
558
Retention cohort experiments: Segmenting loyal vs. fickle users
The Statsig Team
Tue Jan 21 2025
559
Controlling false discoveries: a guide to BH correction in experimentation
The Statsig Team
Tue Jan 21 2025
568
Product differentiation strategies: stand out in crowded markets
The Statsig Team
Sun Jan 19 2025
569
Online power calculation: How to determine sample size for experiments
The Statsig Team
Sat Jan 18 2025
576
Is alpha the significance level? Understanding statistical notation
The Statsig Team
Thu Jan 16 2025
595
Revisiting the analytics definition for strategic decision-making
The Statsig Team
Fri Jan 10 2025
598
A/B testing in high-dimensional spaces: Challenges and solutions
The Statsig Team
Thu Jan 09 2025
599
Designing for scalability: Principles every engineer should know
The Statsig Team
Wed Jan 08 2025
607
How to use Bonferroni correction for multiple hypothesis testing
The Statsig Team
Mon Jan 06 2025
613
Understanding null hypothesis in A/B testing and experimentation
The Statsig Team
Sat Jan 04 2025
617
Multivariate testing vs. A/B testing: Key differences explained
The Statsig Team
Fri Jan 03 2025
629
What is a confidence interval? Definition and practical examples
The Statsig Team
Tue Dec 31 2024
634
Acquisition marketing 101: strategies to win new customers fast
The Statsig Team
Mon Dec 30 2024
641
Experiment vs exploration: What's the difference? (And when to use each)
The Statsig Team
Sun Dec 29 2024
642
Exponential growth vs. linear growth: What it means for your product
The Statsig Team
Sat Dec 28 2024
644
Why mobile developers should put their changes behind feature flags
The Statsig Team
Sat Dec 28 2024
645
Avoiding false positives and false negatives with power analysis
The Statsig Team
Sat Dec 28 2024
646
Balancing acquisition and retention: Dual-path experiment design
The Statsig Team
Fri Dec 27 2024
652
What is minimal detectable effect? How it impacts experiment results
The Statsig Team
Wed Dec 25 2024
668
Finding product-market fit: Step-by-step strategies for startups
The Statsig Team
Sat Dec 21 2024
671
Machine learning monitoring: Keeping models healthy in production
The Statsig Team
Thu Dec 19 2024
672
How increasing the significance level affects statistical power
The Statsig Team
Thu Dec 19 2024
673
What's the difference between longitudinal and cross-sectional cohort tests?
The Statsig Team
Thu Dec 19 2024
674
One-tail vs. two-tailed t-tests: when to use each in A/B testing
The Statsig Team
Wed Dec 18 2024
687
Browser identification strings: how they work and why they matter
The Statsig Team
Mon Dec 16 2024
693
How do I use attribution models to understand user acquisition?
The Statsig Team
Sat Dec 14 2024
697
Balancing short-term vs. long-term metrics in experiment design
The Statsig Team
Wed Dec 11 2024
698
Statistical validity explained: ensuring reliable experiment results
The Statsig Team
Wed Dec 11 2024
713
Leveraging Amplitude and Statsig for enhanced product analytics
The Statsig Team
Fri Dec 06 2024
723
Handling failures in distributed systems: Patterns and anti-patterns
The Statsig Team
Tue Dec 03 2024
729
Data-driven analytics: How to leverage data for smarter decisions
The Statsig Team
Mon Dec 02 2024
730
Accepting the null hypothesis: what it means for your experiment
The Statsig Team
Sun Dec 01 2024
736
A/B testing tools comparison: picking the right one for your product
The Statsig Team
Fri Nov 29 2024
750
The benefits of serverless computing: Why companies are adopting it
The Statsig Team
Sun Nov 24 2024
751
Experimentation beyond A/B tests: Exploring multivariate and sequential testing
The Statsig Team
Sun Nov 24 2024
752
Feature flagging: How to enable controlled releases in development
The Statsig Team
Sun Nov 24 2024
756
Success metrics for a product: How to measure and improve performance
The Statsig Team
Sat Nov 23 2024
762
Feature engineering for infrastructure metrics: CPU and memory optimization
The Statsig Team
Fri Nov 22 2024
763
What is alpha in hypothesis testing? Setting the right threshold
The Statsig Team
Fri Nov 22 2024
770
Weighted confidence interval: formula & step‑by‑step calculation
The Statsig Team
Thu Nov 21 2024
776
Turning cohort observations into actionable experimentation strategies
The Statsig Team
Tue Nov 19 2024
788
User IDs: building a reliable identification system for analytics
The Statsig Team
Fri Nov 15 2024
790
Blending qualitative and quantitative approaches in product experiments
The Statsig Team
Fri Nov 15 2024
792
Sub-processor GDPR compliance: what it means for experimentation
The Statsig Team
Thu Nov 14 2024
800
real-time A/B test monitoring: best practices for quick decisions
The Statsig Team
Sun Nov 10 2024
801
Combining correlation and regression in experiment result analysis
The Statsig Team
Sun Nov 10 2024
807
Data-informed decision making: How to leverage analytics for growth
The Statsig Team
Fri Nov 08 2024
812
Measuring ROI on Bayesian vs. traditional A/B testing approaches
The Statsig Team
Thu Nov 07 2024
814
Bonferroni method: Adjusting for multiple comparisons in hypothesis testing
The Statsig Team
Wed Nov 06 2024
816
Significance test equations explained for product experimenters
The Statsig Team
Wed Nov 06 2024
821
Using RevenueCat data to inform subscription-based experiments in Statsig
The Statsig Team
Tue Nov 05 2024
831
Qualitative insights in quantitative experiments: Balancing both worlds
The Statsig Team
Sat Nov 02 2024
840
Bridging the gap: Using qualitative insights to interpret quantitative results
The Statsig Team
Wed Oct 30 2024
841
Crafting a memorable user journey: Stages, touchpoints, and goals
The Statsig Team
Wed Oct 30 2024
843
Statistical vs. practical significance: why both matter in experiments
The Statsig Team
Tue Oct 29 2024
846
Confounding variables in statistics: How to identify and control them
The Statsig Team
Tue Oct 29 2024
849
Optimizing user flows: Minimizing friction throughout the journey
The Statsig Team
Sun Oct 27 2024
864
How to communicate experiment results to stakeholders using KPIs
The Statsig Team
Wed Oct 23 2024
865
Heap data analytics: maximizing insights through event visualization
The Statsig Team
Wed Oct 23 2024
868
CTR calculations made easy: A quick guide for product marketers
The Statsig Team
Tue Oct 22 2024
875
Product usage analytics: How to measure and optimize user engagement
The Statsig Team
Sun Oct 20 2024
879
Benjamini-Hochberg correction: reducing false positives in testing
The Statsig Team
Fri Oct 18 2024
884
CTR made simple: Understanding click-through rates for beginners
The Statsig Team
Wed Oct 16 2024
889
What is the confidence coefficient? Understanding its role in statistics
The Statsig Team
Tue Oct 15 2024
892
What is the difference between stratified and cluster sampling?
The Statsig Team
Mon Oct 14 2024
893
From data to decisions: How to communicate findings to non-technical teams
The Statsig Team
Mon Oct 14 2024
898
Git branching models: A guide to feature, release, and trunk strategies
The Statsig Team
Sat Oct 12 2024
908
Leading vs. lagging indicators: Experimenting to find the best predictors
The Statsig Team
Wed Oct 09 2024
912
Breaking it down: Data analytics explained for non-technical teams
The Statsig Team
Tue Oct 08 2024
926
Why you should use a power analysis calculator for experimentation
The Statsig Team
Fri Oct 04 2024
939
Enhancing collaboration on feature rollouts with Microsoft Teams and Statsig
The Statsig Team
Tue Oct 01 2024
944
Business analytics for product managers: Turning insights into action
The Statsig Team
Sun Sep 29 2024
948
Measuring funnel success: The key metrics to track at every stage
The Statsig Team
Sat Sep 28 2024
953
Web analytics: Definition, examples, and why it matters for businesses
The Statsig Team
Fri Sep 27 2024
958
Feature flags in Next.js: How to manage feature rollouts efficiently
The Statsig Team
Thu Sep 26 2024
979
Statistical power: What it is and why it's important for A/B testing
The Statsig Team
Sat Sep 21 2024
981
Enhancing data-driven decision-making with Census and Statsig integration
The Statsig Team
Sat Sep 21 2024
990
Faulty causation: How to avoid incorrect cause-and-effect conclusions
The Statsig Team
Thu Sep 19 2024
998
Online experimentation: Running data-driven tests for better decisions
The Statsig Team
Tue Sep 17 2024
999
LTV calculations: How to accurately measure customer lifetime value
The Statsig Team
Mon Sep 16 2024
1004
How to interpret a 95% confidence interval: A guide for analysts
The Statsig Team
Sun Sep 15 2024
1009
Improving team communication around experiments with Slack and Statsig
The Statsig Team
Sat Sep 14 2024
1016
A comprehensive guide to hypothesis testing in product experimentation
The Statsig Team
Fri Sep 13 2024
1029
Integrating Statsig with Visual Studio Code for streamlined development
The Statsig Team
Tue Sep 10 2024
1033
Understanding statistical tests of significance in A/B testing
The Statsig Team
Mon Sep 09 2024
1039
Confidence intervals: What they are and how they help in data analysis
The Statsig Team
Sat Sep 07 2024
1049
Signs you’ve achieved product-market fit (and what to do next)
The Statsig Team
Wed Sep 04 2024
1051
Designing for scalability: experimentation in high-traffic environments
The Statsig Team
Tue Sep 03 2024
1056
Confounding variables in stats: controlling for accurate results
The Statsig Team
Mon Sep 02 2024
1058
Hochberg procedure: controlling false discoveries in experiments
The Statsig Team
Sat Aug 31 2024
1063
Automating issue tracking for experiments using Jira and Statsig
The Statsig Team
Sat Aug 31 2024
1071
Split.io vs. Statsig: which feature flag tool is right for you?
The Statsig Team
Thu Aug 29 2024
1073
Operationalizing data science: From model development to production
The Statsig Team
Wed Aug 28 2024
1080
Event schema example: structuring data for accurate experimentation
The Statsig Team
Tue Aug 27 2024
1104
Boosting your click-through rate: Best practices for growth teams
The Statsig Team
Sat Aug 17 2024
1118
Deploying machine learning models in production: A guide for engineers
The Statsig Team
Tue Aug 13 2024
1122
Using correlation to identify promising hypotheses for A/B tests
The Statsig Team
Mon Aug 12 2024
1151
Understanding different software testing environments (6 examples)
The Statsig Team
Thu Jul 25 2024
1182
Why Brock Lumbard is an excellent BizOps / Strategy Associate at Statsig
The Statsig Team
Mon Jul 15 2024
1188
Types of automated testing: enhance product reliability and UX
The Statsig Team
Fri Jul 12 2024
1190
Rolling Deployment vs Blue Green Deployment - Which is right for you?
The Statsig Team
Wed Jul 10 2024
1229
Understanding the Significance of Statistics in Product Analytics
The Statsig Team
Fri Jul 05 2024
1243
Mastering Conversion Funnel Analysis: Key Insights and Strategies
The Statsig Team
Wed Jul 03 2024
1246
Building a robust data ecosystem: Key components and strategies
The Statsig Team
Wed Jul 03 2024
1259
Unlocking the Power of Statistical Significance in Experiments
The Statsig Team
Tue Jul 02 2024
1274
Enhancing performance with edge config using Statsig and Vercel
The Statsig Team
Fri Jun 28 2024
1282
Understanding Product Usage Segmentation: A Comprehensive Guide
The Statsig Team
Tue Jun 25 2024
1329
Mitigating the impact of data peeking in double-blind experimentation
The Statsig Team
Mon Jun 10 2024
1497
Enhancing user experience with a visual editor in Statsig and Vercel
The Statsig Team
Wed Feb 28 2024
Loved by customers at every stage of growth
See what our users have to say about building with Statsig
"Statsig's experimentation capabilities stand apart from other platforms we've evaluated. The ease of use, simplicity of integration help us efficiently get insight from every experiment we run. Statsig's infrastructure and experimentation workflows have also been crucial in helping us scale to hundreds of experiments across hundreds of millions of users."
Paul Ellwood
Head of Data Engineering
"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
"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
"Statsig has enabled us to quickly understand the impact of the features we ship."
Shannon Priem
Lead PM
"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
"Statsig has been an amazing collaborator as we've scaled. Our product and engineering team have worked on everything from advanced release management to custom workflows to new experimentation features. The Statsig team is fast and incredibly focused on customer needs - mirroring OpenAI so much that they feel like an extension of our team."
Chris Beaumont
Data Scientist
"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
"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'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
"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 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 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
"We use Statsig's analytics to bring rigor to the decision-making process across every team at Wizehire."
Nick Carneiro
CTO
"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
"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
"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
"Statsig has helped accelerate the speed at which we release new features. It enables us to launch new features quickly & turn every release into an A/B test."
Andy Glover
Engineer
"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
"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
"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