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Statsig Perspectives
Thoughts and insights from the team at Statsig
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3
What is the difference between correlation and causation in analytics?
The Statsig Team
Sat Apr 05 2025
23
Pinpointing confounding variables to improve experiment accuracy
The Statsig Team
Tue Mar 18 2025
27
Introduction to Heap Analytics: automated event tracking explained
The Statsig Team
Sat Mar 15 2025
29
Export data definition: when and why to move information externally
The Statsig Team
Fri Mar 14 2025
51
P-values and significance levels: What they mean in hypothesis testing
The Statsig Team
Sun Mar 02 2025
53
How do I effectively share analytics insights with stakeholders?
The Statsig Team
Sat Mar 01 2025
68
Confidence level vs. significance level: key differences explained
The Statsig Team
Sun Feb 23 2025
70
Observability in DevOps: Why it matters for software reliability
The Statsig Team
Fri Feb 21 2025
78
Understanding two-tailed tests: when and why to use them in experiments
The Statsig Team
Mon Feb 17 2025
81
What is website analytics? How to track and improve site performance
The Statsig Team
Mon Feb 17 2025
96
First-order effects in data analysis: What they mean and how to handle them
The Statsig Team
Wed Feb 12 2025
103
Click-through rate demystified: How to turn impressions into actions
The Statsig Team
Mon Feb 10 2025
120
Exponential growth basics: Strategies to skyrocket product adoption
The Statsig Team
Wed Feb 05 2025
130
One-tailed vs. two-tailed hypothesis: Key differences & when to use each
The Statsig Team
Mon Feb 03 2025
131
How to use the Statsig calculator for accurate experiment analysis
The Statsig Team
Mon Feb 03 2025
133
What’s the difference between quantitative and qualitative data?
The Statsig Team
Sun Feb 02 2025
136
Evaluating business analytics software for data-driven decisions
The Statsig Team
Sat Feb 01 2025
137
Deep customer analytics: How to extract insights from your data
The Statsig Team
Fri Jan 31 2025
149
ARR defined: Why annual recurring revenue drives sustainable growth
The Statsig Team
Tue Jan 28 2025
168
Confidence interval percentage: What it means in statistical analysis
The Statsig Team
Sat Jan 25 2025
174
Event tracking fundamentals: why it matters for product insights
The Statsig Team
Thu Jan 23 2025
178
Tracking events in experiments: best practices for accurate data
The Statsig Team
Wed Jan 22 2025
181
Retention cohort experiments: Segmenting loyal vs. fickle users
The Statsig Team
Tue Jan 21 2025
184
Controlling false discoveries: a guide to BH correction in experimentation
The Statsig Team
Tue Jan 21 2025
185
How descriptive analytics lays the foundation for deeper insights
The Statsig Team
Tue Jan 21 2025
187
Making data-backed decisions: A practical intro to business analytics
The Statsig Team
Tue Jan 21 2025
200
Online power calculation: How to determine sample size for experiments
The Statsig Team
Sat Jan 18 2025
205
Is alpha the significance level? Understanding statistical notation
The Statsig Team
Thu Jan 16 2025
225
Revisiting the analytics definition for strategic decision-making
The Statsig Team
Fri Jan 10 2025
227
A/B testing in high-dimensional spaces: Challenges and solutions
The Statsig Team
Thu Jan 09 2025
229
Real-time analytics use cases: driving instant business decisions
The Statsig Team
Thu Jan 09 2025
231
Designing for scalability: Principles every engineer should know
The Statsig Team
Wed Jan 08 2025
238
A/B testing vs. multivariate testing: Which experiment method is best?
The Statsig Team
Tue Jan 07 2025
242
How to use Bonferroni correction for multiple hypothesis testing
The Statsig Team
Mon Jan 06 2025
249
Understanding null hypothesis in A/B testing and experimentation
The Statsig Team
Sat Jan 04 2025
253
Multivariate testing vs. A/B testing: Key differences explained
The Statsig Team
Fri Jan 03 2025
264
What is a confidence interval? Definition and practical examples
The Statsig Team
Tue Dec 31 2024
277
Experiment vs exploration: What's the difference? (And when to use each)
The Statsig Team
Sun Dec 29 2024
280
Exponential growth vs. linear growth: What it means for your product
The Statsig Team
Sat Dec 28 2024
281
Why mobile developers should put their changes behind feature flags
The Statsig Team
Sat Dec 28 2024
283
Avoiding false positives and false negatives with power analysis
The Statsig Team
Sat Dec 28 2024
287
Balancing acquisition and retention: Dual-path experiment design
The Statsig Team
Fri Dec 27 2024
292
Using survey data as a springboard for quantitative experiments
The Statsig Team
Thu Dec 26 2024
294
What is minimal detectable effect? How it impacts experiment results
The Statsig Team
Wed Dec 25 2024
302
Exploring the equation for exponential growth in practical terms
The Statsig Team
Mon Dec 23 2024
310
Finding product-market fit: Step-by-step strategies for startups
The Statsig Team
Sat Dec 21 2024
312
Machine learning monitoring: Keeping models healthy in production
The Statsig Team
Thu Dec 19 2024
313
What's the difference between longitudinal and cross-sectional cohort tests?
The Statsig Team
Thu Dec 19 2024
314
How increasing the significance level affects statistical power
The Statsig Team
Thu Dec 19 2024
319
One-tail vs. two-tailed t-tests: when to use each in A/B testing
The Statsig Team
Wed Dec 18 2024
327
Browser identification strings: how they work and why they matter
The Statsig Team
Mon Dec 16 2024
330
Data analytics 101: Transforming raw information into product growth
The Statsig Team
Sun Dec 15 2024
334
Raising your CTR: Practical tips to surpass averages and grow faster
The Statsig Team
Sat Dec 14 2024
335
How do I use attribution models to understand user acquisition?
The Statsig Team
Sat Dec 14 2024
342
Balancing short-term vs. long-term metrics in experiment design
The Statsig Team
Wed Dec 11 2024
343
Statistical validity explained: ensuring reliable experiment results
The Statsig Team
Wed Dec 11 2024
360
Leveraging Amplitude and Statsig for enhanced product analytics
The Statsig Team
Fri Dec 06 2024
375
Handling failures in distributed systems: Patterns and anti-patterns
The Statsig Team
Tue Dec 03 2024
381
Data-driven analytics: How to leverage data for smarter decisions
The Statsig Team
Mon Dec 02 2024
384
Accepting the null hypothesis: what it means for your experiment
The Statsig Team
Sun Dec 01 2024
393
A/B testing tools comparison: picking the right one for your product
The Statsig Team
Fri Nov 29 2024
399
How might an organization apply analytics at a production scale?
The Statsig Team
Mon Nov 25 2024
406
The benefits of serverless computing: Why companies are adopting it
The Statsig Team
Sun Nov 24 2024
408
Experimentation beyond A/B tests: Exploring multivariate and sequential testing
The Statsig Team
Sun Nov 24 2024
409
Feature flagging: How to enable controlled releases in development
The Statsig Team
Sun Nov 24 2024
413
Success metrics for a product: How to measure and improve performance
The Statsig Team
Sat Nov 23 2024
414
What is alpha in hypothesis testing? Setting the right threshold
The Statsig Team
Fri Nov 22 2024
421
Feature engineering for infrastructure metrics: CPU and memory optimization
The Statsig Team
Fri Nov 22 2024
429
KPI deep-dive: conversions, retention, and engagement in experiments
The Statsig Team
Wed Nov 20 2024
431
Why functional testing matters for reliable product performance
The Statsig Team
Wed Nov 20 2024
437
Turning cohort observations into actionable experimentation strategies
The Statsig Team
Tue Nov 19 2024
451
User IDs: building a reliable identification system for analytics
The Statsig Team
Fri Nov 15 2024
452
Blending qualitative and quantitative approaches in product experiments
The Statsig Team
Fri Nov 15 2024
456
Sub-processor GDPR compliance: what it means for experimentation
The Statsig Team
Thu Nov 14 2024
468
Combining correlation and regression in experiment result analysis
The Statsig Team
Sun Nov 10 2024
470
real-time A/B test monitoring: best practices for quick decisions
The Statsig Team
Sun Nov 10 2024
476
Data-informed decision making: How to leverage analytics for growth
The Statsig Team
Fri Nov 08 2024
481
Measuring ROI on Bayesian vs. traditional A/B testing approaches
The Statsig Team
Thu Nov 07 2024
487
Bonferroni method: Adjusting for multiple comparisons in hypothesis testing
The Statsig Team
Wed Nov 06 2024
489
Significance test equations explained for product experimenters
The Statsig Team
Wed Nov 06 2024
493
Using RevenueCat data to inform subscription-based experiments in Statsig
The Statsig Team
Tue Nov 05 2024
499
HEART TPR: expanding the Google HEART framework with true positive rate
The Statsig Team
Sun Nov 03 2024
506
Qualitative insights in quantitative experiments: Balancing both worlds
The Statsig Team
Sat Nov 02 2024
516
Crafting a memorable user journey: Stages, touchpoints, and goals
The Statsig Team
Wed Oct 30 2024
520
Bridging the gap: Using qualitative insights to interpret quantitative results
The Statsig Team
Wed Oct 30 2024
524
Statistical vs. practical significance: why both matter in experiments
The Statsig Team
Tue Oct 29 2024
525
Confounding variables in statistics: How to identify and control them
The Statsig Team
Tue Oct 29 2024
528
Optimizing user flows: Minimizing friction throughout the journey
The Statsig Team
Sun Oct 27 2024
529
How to scale your analytics from basic dashboards to real insights
The Statsig Team
Sun Oct 27 2024
545
Heap data analytics: maximizing insights through event visualization
The Statsig Team
Wed Oct 23 2024
546
How to communicate experiment results to stakeholders using KPIs
The Statsig Team
Wed Oct 23 2024
549
CTR calculations made easy: A quick guide for product marketers
The Statsig Team
Tue Oct 22 2024
557
Product usage analytics: How to measure and optimize user engagement
The Statsig Team
Sun Oct 20 2024
563
Website experimentation: How to optimize performance with A/B testing
The Statsig Team
Sat Oct 19 2024
566
Benjamini-Hochberg correction: reducing false positives in testing
The Statsig Team
Fri Oct 18 2024
574
CTR made simple: Understanding click-through rates for beginners
The Statsig Team
Wed Oct 16 2024
580
What is the confidence coefficient? Understanding its role in statistics
The Statsig Team
Tue Oct 15 2024
582
From data to decisions: How to communicate findings to non-technical teams
The Statsig Team
Mon Oct 14 2024
583
What is the difference between stratified and cluster sampling?
The Statsig Team
Mon Oct 14 2024
589
Git branching models: A guide to feature, release, and trunk strategies
The Statsig Team
Sat Oct 12 2024
598
Leading vs. lagging indicators: Experimenting to find the best predictors
The Statsig Team
Wed Oct 09 2024
603
Breaking it down: Data analytics explained for non-technical teams
The Statsig Team
Tue Oct 08 2024
621
Why you should use a power analysis calculator for experimentation
The Statsig Team
Fri Oct 04 2024
635
Enhancing collaboration on feature rollouts with Microsoft Teams and Statsig
The Statsig Team
Tue Oct 01 2024
642
Business analytics for product managers: Turning insights into action
The Statsig Team
Sun Sep 29 2024
643
Boosting CTR through better user experiences and targeted content
The Statsig Team
Sun Sep 29 2024
650
Measuring funnel success: The key metrics to track at every stage
The Statsig Team
Sat Sep 28 2024
652
Web analytics: Definition, examples, and why it matters for businesses
The Statsig Team
Fri Sep 27 2024
655
Feature flags in Next.js: How to manage feature rollouts efficiently
The Statsig Team
Thu Sep 26 2024
679
Statistical power: What it is and why it's important for A/B testing
The Statsig Team
Sat Sep 21 2024
682
Enhancing data-driven decision-making with Census and Statsig integration
The Statsig Team
Sat Sep 21 2024
692
Faulty causation: How to avoid incorrect cause-and-effect conclusions
The Statsig Team
Thu Sep 19 2024
697
Online experimentation: Running data-driven tests for better decisions
The Statsig Team
Tue Sep 17 2024
702
LTV calculations: How to accurately measure customer lifetime value
The Statsig Team
Mon Sep 16 2024
704
How to interpret a 95% confidence interval: A guide for analysts
The Statsig Team
Sun Sep 15 2024
710
Improving team communication around experiments with Slack and Statsig
The Statsig Team
Sat Sep 14 2024
717
A comprehensive guide to hypothesis testing in product experimentation
The Statsig Team
Fri Sep 13 2024
730
Integrating Statsig with Visual Studio Code for streamlined development
The Statsig Team
Tue Sep 10 2024
741
Confidence intervals: What they are and how they help in data analysis
The Statsig Team
Sat Sep 07 2024
757
Behavioral data examples: how to leverage them in experimentation
The Statsig Team
Tue Sep 03 2024
759
Designing for scalability: experimentation in high-traffic environments
The Statsig Team
Tue Sep 03 2024
762
Confounding variables in stats: controlling for accurate results
The Statsig Team
Mon Sep 02 2024
763
Funnel optimization tactics: Converting curious visitors into loyal users
The Statsig Team
Sun Sep 01 2024
770
Automating issue tracking for experiments using Jira and Statsig
The Statsig Team
Sat Aug 31 2024
771
Hochberg procedure: controlling false discoveries in experiments
The Statsig Team
Sat Aug 31 2024
777
Split.io vs. Statsig: which feature flag tool is right for you?
The Statsig Team
Thu Aug 29 2024
784
Operationalizing data science: From model development to production
The Statsig Team
Wed Aug 28 2024
789
Event schema example: structuring data for accurate experimentation
The Statsig Team
Tue Aug 27 2024
818
Boosting your click-through rate: Best practices for growth teams
The Statsig Team
Sat Aug 17 2024
836
Deploying machine learning models in production: A guide for engineers
The Statsig Team
Tue Aug 13 2024
837
Using correlation to identify promising hypotheses for A/B tests
The Statsig Team
Mon Aug 12 2024
867
Improving experimentation quality with real-time error monitoring via Bugsnag
The Statsig Team
Tue Jul 30 2024
876
Understanding different software testing environments (6 examples)
The Statsig Team
Thu Jul 25 2024
884
Infrastructure as code: Automating cloud deployments with Terraform
The Statsig Team
Tue Jul 23 2024
909
Why Brock Lumbard is an excellent BizOps / Strategy Associate at Statsig
The Statsig Team
Mon Jul 15 2024
922
Rolling Deployment vs Blue Green Deployment - Which is right for you?
The Statsig Team
Wed Jul 10 2024
962
Understanding the Significance of Statistics in Product Analytics
The Statsig Team
Fri Jul 05 2024
979
Mastering Conversion Funnel Analysis: Key Insights and Strategies
The Statsig Team
Wed Jul 03 2024
982
Building a robust data ecosystem: Key components and strategies
The Statsig Team
Wed Jul 03 2024
1010
Enhancing performance with edge config using Statsig and Vercel
The Statsig Team
Fri Jun 28 2024
1018
Understanding Product Usage Segmentation: A Comprehensive Guide
The Statsig Team
Tue Jun 25 2024
1070
Mitigating the impact of data peeking in double-blind experimentation
The Statsig Team
Mon Jun 10 2024
1265
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
"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
"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
"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
"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 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
"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
"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
"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