Products
Solutions
Resources
Statsig Perspectives
Thoughts and insights from the team at Statsig
All Posts
9
Correlation Definition: What It Means and How to Use It in Analytics
The Statsig Team
Tue Nov 18 2025
12
How Rappi Scales Experimentation with Feature Flags and AI Analytics
The Statsig Team
Tue Nov 18 2025
13
A/B Testing Statistics: When to Use the t-test vs. z-test in Practice
The Statsig Team
Tue Nov 18 2025
14
Types of Data Explained: Structured, Unstructured, and Semi-Structured
The Statsig Team
Tue Nov 18 2025
15
How to Build a Data-Driven Journey Map for B2B SaaS Experimentation
The Statsig Team
Tue Nov 18 2025
16
Prescriptive Analytics for Product Teams: From Insight to Impact
The Statsig Team
Tue Nov 18 2025
20
Optimizely for Experimentation: Implementing Feature Flags at Scale
The Statsig Team
Tue Nov 18 2025
30
Data-Driven Product Roadmap: Prioritization Frameworks That Work
The Statsig Team
Tue Nov 18 2025
35
How to Quantify Upsell Impact with Experimentation and Feature Flags
The Statsig Team
Tue Nov 18 2025
36
Blue-Green Deployment: Data-Driven Guide to Zero-Downtime Releases
The Statsig Team
Tue Nov 18 2025
37
Democratization in AI Evaluation: Metrics, Governance, and Scale
The Statsig Team
Tue Nov 18 2025
40
Product Development Process: A Data-Driven Framework for SaaS Teams
The Statsig Team
Tue Nov 18 2025
43
Website Redesign Metrics: How to Measure Impact with A/B Testing
The Statsig Team
Tue Nov 18 2025
49
Digital Asset Management ROI: Metrics That Matter for Enterprises
The Statsig Team
Tue Nov 18 2025
51
Data Types in Experimentation: A Technical Guide for Product Teams
The Statsig Team
Tue Nov 18 2025
53
Data-Driven Differentiation Strategy for B2B SaaS Metrics That Matter
The Statsig Team
Tue Nov 18 2025
54
Product-Led Growth: Metrics, Experiments, and Benchmarks That Matter
The Statsig Team
Tue Nov 18 2025
59
Retention Analytics: How to Measure and Improve with Experiments
The Statsig Team
Tue Nov 18 2025
64
Statistical Significance in A/B Testing: What Product Teams Need
The Statsig Team
Fri Nov 07 2025
66
Null Hypothesis in A/B Testing: Definition, Examples, Best Practices
The Statsig Team
Fri Nov 07 2025
69
Sequential Testing vs Fixed-Horizon Tests: Trade-offs and Use Cases
The Statsig Team
Fri Nov 07 2025
71
Product Analytics Tool Comparison: Features, Pricing, Integrations
The Statsig Team
Fri Nov 07 2025
72
Product Analytics Tool Comparison: Metrics, Integrations, and ROI
The Statsig Team
Fri Nov 07 2025
74
How to Know If Something Is Statistically Significant in A/B Tests
The Statsig Team
Fri Nov 07 2025
77
Funnel Metrics: A Technical Guide to Measuring Conversion Health
The Statsig Team
Fri Nov 07 2025
82
LaunchDarkly Competitors: Data-Driven Comparison of Top Alternatives
The Statsig Team
Fri Nov 07 2025
86
Bonferroni Correction Explained: Control False Positives in A/B Tests
The Statsig Team
Fri Nov 07 2025
87
Conversion Optimization with Feature Flags: A Data-Driven Approach
The Statsig Team
Fri Nov 07 2025
90
Product Led Growth: Metrics, Benchmarks, and Implementation Guide
The Statsig Team
Fri Nov 07 2025
92
Website Optimization Tools Compared: A/B Testing, Flags, Analytics
The Statsig Team
Fri Nov 07 2025
93
Product led growth metrics: How to measure activation and retention
The Statsig Team
Fri Nov 07 2025
94
Website Optimization Tools Compared: Experimentation and Analytics
The Statsig Team
Fri Nov 07 2025
95
Power Analysis for A/B Testing: How to Size Experiments Correctly
The Statsig Team
Fri Nov 07 2025
107
Few-Shot Prompting: Techniques and Evaluation Metrics That Matter
The Statsig Team
Fri Nov 07 2025
113
Minimum Detectable Effect: How to Calculate and Use It in A/B Tests
The Statsig Team
Fri Nov 07 2025
115
Sample Size Calculator: How to Choose the Right Tool for A/B Tests
The Statsig Team
Fri Nov 07 2025
116
Confidence Interval in A/B Testing: How to Calculate and Interpret
The Statsig Team
Fri Nov 07 2025
118
Frequentist vs Bayesian: Choosing the Right Approach for A/B Tests
The Statsig Team
Fri Nov 07 2025
120
Type 2 Error in A/B Testing: How to Detect and Reduce False Negatives
The Statsig Team
Fri Nov 07 2025
230
How Google's June outage could have been fixed with a feature flag
The Statsig Team
Sat Jun 28 2025
232
Un guide rapide sur la disparité du ratio d'échantillonnage (SRM)
The Statsig Team
Tue Jun 24 2025
258
Contrôler vos erreurs de type I : Bonferroni et Benjamini-Hochberg
The Statsig Team
Tue Jun 24 2025
383
MDE in A/B testing: Setting realistic expectations for your experiments
The Statsig Team
Mon Jun 23 2025
384
The multiple comparisons problem: Why running many A/B tests requires special care
The Statsig Team
Mon Jun 23 2025
385
Sequential testing: How to peek at A/B test results without ruining validity
The Statsig Team
Mon Jun 23 2025
387
Confidence intervals in A/B testing: Beyond simple win/loss metrics
The Statsig Team
Mon Jun 23 2025
392
CTR optimization: A/B testing your way to higher click-through rates
The Statsig Team
Mon Jun 23 2025
393
A/B testing revenue metrics: Optimizing for long-term business value
The Statsig Team
Mon Jun 23 2025
394
Establishing baseline metrics: The starting point for every A/B test
The Statsig Team
Mon Jun 23 2025
398
Chi‑square versus t‑test: when and how to use each statistical test
The Statsig Team
Wed Apr 16 2025
400
Product functionality versus benefits: how to communicate value
The Statsig Team
Sun Apr 13 2025
416
Pinpointing confounding variables to improve experiment accuracy
The Statsig Team
Tue Mar 18 2025
417
Introduction to Heap Analytics: automated event tracking explained
The Statsig Team
Sat Mar 15 2025
420
Export data definition: when and why to move information externally
The Statsig Team
Fri Mar 14 2025
437
LTV analysis: How to measure customer lifetime value accurately
The Statsig Team
Sun Mar 02 2025
438
P-values and significance levels: What they mean in hypothesis testing
The Statsig Team
Sun Mar 02 2025
439
How do I effectively share analytics insights with stakeholders?
The Statsig Team
Sat Mar 01 2025
442
Average e‑commerce conversion rate by industry (2025 benchmarks)
The Statsig Team
Wed Feb 26 2025
445
Confidence level vs. significance level: key differences explained
The Statsig Team
Sun Feb 23 2025
451
What is website analytics? How to track and improve site performance
The Statsig Team
Mon Feb 17 2025
453
Understanding two-tailed tests: when and why to use them in experiments
The Statsig Team
Mon Feb 17 2025
464
First-order effects in data analysis: What they mean and how to handle them
The Statsig Team
Wed Feb 12 2025
466
Click-through rate demystified: How to turn impressions into actions
The Statsig Team
Mon Feb 10 2025
484
How to use the Statsig calculator for accurate experiment analysis
The Statsig Team
Mon Feb 03 2025
485
SQL data types explained: MySQL, Postgres & SQL Server cheat sheet
The Statsig Team
Mon Feb 03 2025
486
One-tailed vs. two-tailed hypothesis: Key differences & when to use each
The Statsig Team
Mon Feb 03 2025
492
Deep customer analytics: How to extract insights from your data
The Statsig Team
Fri Jan 31 2025
499
ARR defined: Why annual recurring revenue drives sustainable growth
The Statsig Team
Tue Jan 28 2025
509
Confidence interval percentage: What it means in statistical analysis
The Statsig Team
Sat Jan 25 2025
515
Event tracking fundamentals: why it matters for product insights
The Statsig Team
Thu Jan 23 2025
516
Tracking events in experiments: best practices for accurate data
The Statsig Team
Wed Jan 22 2025
520
Controlling false discoveries: a guide to BH correction in experimentation
The Statsig Team
Tue Jan 21 2025
526
Product differentiation strategies: stand out in crowded markets
The Statsig Team
Sun Jan 19 2025
528
Online power calculation: How to determine sample size for experiments
The Statsig Team
Sat Jan 18 2025
533
Is alpha the significance level? Understanding statistical notation
The Statsig Team
Thu Jan 16 2025
549
A/B testing in high-dimensional spaces: Challenges and solutions
The Statsig Team
Thu Jan 09 2025
552
Designing for scalability: Principles every engineer should know
The Statsig Team
Wed Jan 08 2025
557
How to use Bonferroni correction for multiple hypothesis testing
The Statsig Team
Mon Jan 06 2025
564
Understanding null hypothesis in A/B testing and experimentation
The Statsig Team
Sat Jan 04 2025
575
What is a confidence interval? Definition and practical examples
The Statsig Team
Tue Dec 31 2024
579
Experiment vs exploration: What's the difference? (And when to use each)
The Statsig Team
Sun Dec 29 2024
581
Avoiding false positives and false negatives with power analysis
The Statsig Team
Sat Dec 28 2024
582
Exponential growth vs. linear growth: What it means for your product
The Statsig Team
Sat Dec 28 2024
587
What is minimal detectable effect? How it impacts experiment results
The Statsig Team
Wed Dec 25 2024
601
What's the difference between longitudinal and cross-sectional cohort tests?
The Statsig Team
Thu Dec 19 2024
602
How increasing the significance level affects statistical power
The Statsig Team
Thu Dec 19 2024
603
Machine learning monitoring: Keeping models healthy in production
The Statsig Team
Thu Dec 19 2024
605
One-tail vs. two-tailed t-tests: when to use each in A/B testing
The Statsig Team
Wed Dec 18 2024
613
Browser identification strings: how they work and why they matter
The Statsig Team
Mon Dec 16 2024
617
How do I use attribution models to understand user acquisition?
The Statsig Team
Sat Dec 14 2024
620
Statistical validity explained: ensuring reliable experiment results
The Statsig Team
Wed Dec 11 2024
621
Balancing short-term vs. long-term metrics in experiment design
The Statsig Team
Wed Dec 11 2024
640
Handling failures in distributed systems: Patterns and anti-patterns
The Statsig Team
Tue Dec 03 2024
648
Accepting the null hypothesis: what it means for your experiment
The Statsig Team
Sun Dec 01 2024
651
A/B testing tools comparison: picking the right one for your product
The Statsig Team
Fri Nov 29 2024
664
Experimentation beyond A/B tests: Exploring multivariate and sequential testing
The Statsig Team
Sun Nov 24 2024
666
Feature flagging: How to enable controlled releases in development
The Statsig Team
Sun Nov 24 2024
671
What is alpha in hypothesis testing? Setting the right threshold
The Statsig Team
Fri Nov 22 2024
676
Weighted confidence interval: formula & step‑by‑step calculation
The Statsig Team
Thu Nov 21 2024
689
User IDs: building a reliable identification system for analytics
The Statsig Team
Fri Nov 15 2024
692
Sub-processor GDPR compliance: what it means for experimentation
The Statsig Team
Thu Nov 14 2024
697
Combining correlation and regression in experiment result analysis
The Statsig Team
Sun Nov 10 2024
705
Measuring ROI on Bayesian vs. traditional A/B testing approaches
The Statsig Team
Thu Nov 07 2024
707
Bonferroni method: Adjusting for multiple comparisons in hypothesis testing
The Statsig Team
Wed Nov 06 2024
718
Qualitative insights in quantitative experiments: Balancing both worlds
The Statsig Team
Sat Nov 02 2024
728
Confounding variables in statistics: How to identify and control them
The Statsig Team
Tue Oct 29 2024
729
Statistical vs. practical significance: why both matter in experiments
The Statsig Team
Tue Oct 29 2024
730
Optimizing user flows: Minimizing friction throughout the journey
The Statsig Team
Sun Oct 27 2024
741
How to communicate experiment results to stakeholders using KPIs
The Statsig Team
Wed Oct 23 2024
744
CTR calculations made easy: A quick guide for product marketers
The Statsig Team
Tue Oct 22 2024
749
Product usage analytics: How to measure and optimize user engagement
The Statsig Team
Sun Oct 20 2024
754
Benjamini-Hochberg correction: reducing false positives in testing
The Statsig Team
Fri Oct 18 2024
759
CTR made simple: Understanding click-through rates for beginners
The Statsig Team
Wed Oct 16 2024
760
What is the confidence coefficient? Understanding its role in statistics
The Statsig Team
Tue Oct 15 2024
763
What is the difference between stratified and cluster sampling?
The Statsig Team
Mon Oct 14 2024
764
From data to decisions: How to communicate findings to non-technical teams
The Statsig Team
Mon Oct 14 2024
766
Git branching models: A guide to feature, release, and trunk strategies
The Statsig Team
Sat Oct 12 2024
770
Leading vs. lagging indicators: Experimenting to find the best predictors
The Statsig Team
Wed Oct 09 2024
773
Breaking it down: Data analytics explained for non-technical teams
The Statsig Team
Tue Oct 08 2024
783
Why you should use a power analysis calculator for experimentation
The Statsig Team
Fri Oct 04 2024
794
Web analytics: Definition, examples, and why it matters for businesses
The Statsig Team
Fri Sep 27 2024
796
Feature flags in Next.js: How to manage feature rollouts efficiently
The Statsig Team
Thu Sep 26 2024
810
Statistical power: What it is and why it's important for A/B testing
The Statsig Team
Sat Sep 21 2024
812
Enhancing data-driven decision-making with Census and Statsig integration
The Statsig Team
Sat Sep 21 2024
821
Faulty causation: How to avoid incorrect cause-and-effect conclusions
The Statsig Team
Thu Sep 19 2024
828
LTV calculations: How to accurately measure customer lifetime value
The Statsig Team
Mon Sep 16 2024
831
How to interpret a 95% confidence interval: A guide for analysts
The Statsig Team
Sun Sep 15 2024
836
Improving team communication around experiments with Slack and Statsig
The Statsig Team
Sat Sep 14 2024
839
A comprehensive guide to hypothesis testing in product experimentation
The Statsig Team
Fri Sep 13 2024
852
Confidence intervals: What they are and how they help in data analysis
The Statsig Team
Sat Sep 07 2024
859
Confounding variables in stats: controlling for accurate results
The Statsig Team
Mon Sep 02 2024
860
Automating issue tracking for experiments using Jira and Statsig
The Statsig Team
Sat Aug 31 2024
863
Hochberg procedure: controlling false discoveries in experiments
The Statsig Team
Sat Aug 31 2024
865
Split.io vs. Statsig: which feature flag tool is right for you?
The Statsig Team
Thu Aug 29 2024
869
Operationalizing data science: From model development to production
The Statsig Team
Wed Aug 28 2024
872
Event schema example: structuring data for accurate experimentation
The Statsig Team
Tue Aug 27 2024
887
Deploying machine learning models in production: A guide for engineers
The Statsig Team
Tue Aug 13 2024
904
Understanding different software testing environments (6 examples)
The Statsig Team
Thu Jul 25 2024
921
Why Brock Lumbard is an excellent BizOps / Strategy Associate at Statsig
The Statsig Team
Mon Jul 15 2024
952
Understanding the Significance of Statistics in Product Analytics
The Statsig Team
Fri Jul 05 2024
981
Understanding Product Usage Segmentation: A Comprehensive Guide
The Statsig Team
Tue Jun 25 2024
1003
Mitigating the impact of data peeking in double-blind experimentation
The Statsig Team
Mon Jun 10 2024
1082
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