Podcast URL updates

Author: kavaivaleri

_podcast/s07e06-ab-testing.md …b-testing-and-product-experimentation.md_podcast/s07e06-ab-testing.md renamed to _podcast/ab-testing-and-product-experimentation.md _podcast/s07e06-ab-testing.md renamed to _podcast/ab-testing-and-product-experimentation.md

@@ -1,41 +1,115 @@
 ---
+title: 'Product Analytics & A/B Testing: Causality, Metrics, Power Analysis, A/A Tests'
+short: A/B Testing
+season: 7
 episode: 6
 guests:
 - jakobgraff
-short: A/B Testing
-title: 'Product Analytics & A/B Testing: Causality, Metrics, Power Analysis, A/A Tests'
-description: 'Master product analytics, A/B testing & power analysis: design stable
-  metrics, validate randomization with A/A tests, plan sample size to de-risk features.'
-intro: How do you design product experiments that truly establish causality and avoid
-  costly false conclusions? In this episode, Jakob Graff — Director of Data Science
-  and Data Analytics at diconium, with prior analytics leadership at Inkitt, Babbel,
-  King and a background in econometrics — walks through practical product analytics
-  and A/B testing strategies focused on causality and reliable metrics. <br><br> We
-  cover why randomized experiments mirror clinical trials, how experimentation de-risks
-  features and builds organizational learning, and a concrete case study on subscription
-  vs. points revenue metric design. Jakob explains experimentation platform trade-offs
-  (third-party vs. in-house), traffic splitters, assignment tracking, and why A/A
-  tests validate system trust. You’ll hear best practices for first tests (two-group
-  simplicity), metric selection considering noise and seasonality, and how to plan
-  duration with power analysis and sample-size calculations. The discussion also compares
-  z/t/nonparametric tests, p-value intuition from A/A comparisons, frequentist vs
-  Bayesian perspectives, and multi-armed test considerations. <br><br> Listen to learn
-  practical steps for designing randomized experiments, selecting stable metrics,
-  planning sample sizes, and interpreting results so your product analytics and A/B
-  testing produce actionable, causal insights.
-topics:
-- data science
-- practices
+image: images/podcast/s07e06-ab-testing.jpg
 ids:
   anchor: AB-Testing---Jakob-Graff-e1eq73v
   youtube: 0Gqx1LtqRZU
-image: images/podcast/s07e06-ab-testing.jpg
 links:
   anchor: https://anchor.fm/datatalksclub/episodes/AB-Testing---Jakob-Graff-e1eq73v
   apple: https://podcasts.apple.com/us/podcast/a-b-testing-jakob-graff/id1541710331?i=1000552243668
   spotify: https://open.spotify.com/episode/3LhBOO1UANCGbOwkntZt4j
   youtube: https://www.youtube.com/watch?v=0Gqx1LtqRZU
-season: 7
+
+description: 'Master product analytics, A/B testing & power analysis: design stable metrics, validate randomization with A/A tests, plan sample size to de-risk features.'
+intro: How do you design product experiments that truly establish causality and avoid costly false conclusions? In this episode, Jakob Graff — Director of Data Science and Data Analytics at diconium, with prior analytics leadership at Inkitt, Babbel, King and a background in econometrics — walks through practical product analytics and A/B testing strategies focused on causality and reliable metrics. <br><br> We cover why randomized experiments mirror clinical trials, how experimentation de-risks features and builds organizational learning, and a concrete case study on subscription vs. points revenue metric design. Jakob explains experimentation platform trade-offs (third-party vs. in-house), traffic splitters, assignment tracking, and why A/A tests validate system trust. You’ll hear best practices for first tests (two-group simplicity), metric selection considering noise and seasonality, and how to plan duration with power analysis and sample-size calculations. The discussion also compares z/t/nonparametric tests, p-value intuition from A/A comparisons, frequentist vs Bayesian perspectives, and multi-armed test considerations. <br><br> Listen to learn practical steps for designing randomized experiments, selecting stable metrics, planning sample sizes, and interpreting results so your product analytics and A/B testing produce actionable, causal insights
+topics:
+- data science
+- practices
+dateadded: 2022-02-27
+
+duration: PT01H03M37S
+
+quotableClips:
+- name: Podcast Introduction
+  startOffset: 0
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=0
+  endOffset: 63
+- name: Guest Background & Career Transition to Data Science
+  startOffset: 63
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=63
+  endOffset: 311
+- name: 'Econometrics to Product Analytics: Causality Emphasis'
+  startOffset: 311
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=311
+  endOffset: 493
+- name: 'A/B Testing Explained: Clinical Trials Analogy & Randomization'
+  startOffset: 493
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=493
+  endOffset: 708
+- name: 'Experimentation Purpose: Establishing Causality & Controlling Noise'
+  startOffset: 708
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=708
+  endOffset: 867
+- name: 'Case Study: Subscription vs Points — Revenue Metric Design'
+  startOffset: 867
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=867
+  endOffset: 1086
+- name: De-risking Features & Building Organizational Learning with Experiments
+  startOffset: 1086
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=1086
+  endOffset: 1434
+- name: 'Experimentation Platform Choices: Third-Party vs In-House'
+  startOffset: 1434
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=1434
+  endOffset: 1484
+- name: Traffic Splitter Implementation, Assignment Tracking & Monitoring
+  startOffset: 1484
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=1484
+  endOffset: 1672
+- name: 'A/A Testing: Validating Randomization and System Trust'
+  startOffset: 1672
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=1672
+  endOffset: 1805
+- name: 'First Test Best Practices: Two-Group Design & Simplicity'
+  startOffset: 1805
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=1805
+  endOffset: 2003
+- name: 'Metric Selection: Noise, Stability, Seasonality & Business Cycles'
+  startOffset: 2003
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=2003
+  endOffset: 2264
+- name: 'Test Duration & Power Analysis: Sample Size Planning'
+  startOffset: 2264
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=2264
+  endOffset: 2423
+- name: 'Statistical Tests Overview: Z-test, T-test, and Nonparametric Options'
+  startOffset: 2423
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=2423
+  endOffset: 2679
+- name: 'Data Distribution Checks: Histograms, Tails, and Visualization'
+  startOffset: 2679
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=2679
+  endOffset: 2864
+- name: 'P-value Intuition: Explaining Significance via A/A Comparison'
+  startOffset: 2864
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=2864
+  endOffset: 3115
+- name: 'Frequentist vs Bayesian Testing: Credible Intervals, Priors & Costs'
+  startOffset: 3115
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=3115
+  endOffset: 3548
+- name: 'Multi-armed Tests (A/B/C/D): Duration, Power, and Multiple Comparisons'
+  startOffset: 3548
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=3548
+  endOffset: 3772
+- name: Practical Experimentation Tips & Analogies (Pizza Dough)
+  startOffset: 3772
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=3772
+  endOffset: 3839
+- name: Hiring, Resources & Contact Information
+  startOffset: 3839
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=3839
+  endOffset: 3880
+- name: Episode Wrap-up and Key Takeaways
+  startOffset: 3880
+  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=3880
+  endOffset: 3817
+
 transcript:
 - header: Podcast Introduction
 - header: Guest Background & Career Transition to Data Science
@@ -1009,91 +1083,4 @@ transcript:
   sec: 3880
   time: '1:04:40'
   who: Alexey
-dateadded: '2022-02-27'
-duration: PT01H03M37S
-quotableClips:
-- name: Podcast Introduction
-  startOffset: 0
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=0
-  endOffset: 63
-- name: Guest Background & Career Transition to Data Science
-  startOffset: 63
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=63
-  endOffset: 311
-- name: 'Econometrics to Product Analytics: Causality Emphasis'
-  startOffset: 311
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=311
-  endOffset: 493
-- name: 'A/B Testing Explained: Clinical Trials Analogy & Randomization'
-  startOffset: 493
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=493
-  endOffset: 708
-- name: 'Experimentation Purpose: Establishing Causality & Controlling Noise'
-  startOffset: 708
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=708
-  endOffset: 867
-- name: 'Case Study: Subscription vs Points — Revenue Metric Design'
-  startOffset: 867
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=867
-  endOffset: 1086
-- name: De-risking Features & Building Organizational Learning with Experiments
-  startOffset: 1086
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=1086
-  endOffset: 1434
-- name: 'Experimentation Platform Choices: Third-Party vs In-House'
-  startOffset: 1434
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=1434
-  endOffset: 1484
-- name: Traffic Splitter Implementation, Assignment Tracking & Monitoring
-  startOffset: 1484
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=1484
-  endOffset: 1672
-- name: 'A/A Testing: Validating Randomization and System Trust'
-  startOffset: 1672
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=1672
-  endOffset: 1805
-- name: 'First Test Best Practices: Two-Group Design & Simplicity'
-  startOffset: 1805
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=1805
-  endOffset: 2003
-- name: 'Metric Selection: Noise, Stability, Seasonality & Business Cycles'
-  startOffset: 2003
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=2003
-  endOffset: 2264
-- name: 'Test Duration & Power Analysis: Sample Size Planning'
-  startOffset: 2264
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=2264
-  endOffset: 2423
-- name: 'Statistical Tests Overview: Z-test, T-test, and Nonparametric Options'
-  startOffset: 2423
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=2423
-  endOffset: 2679
-- name: 'Data Distribution Checks: Histograms, Tails, and Visualization'
-  startOffset: 2679
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=2679
-  endOffset: 2864
-- name: 'P-value Intuition: Explaining Significance via A/A Comparison'
-  startOffset: 2864
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=2864
-  endOffset: 3115
-- name: 'Frequentist vs Bayesian Testing: Credible Intervals, Priors & Costs'
-  startOffset: 3115
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=3115
-  endOffset: 3548
-- name: 'Multi-armed Tests (A/B/C/D): Duration, Power, and Multiple Comparisons'
-  startOffset: 3548
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=3548
-  endOffset: 3772
-- name: Practical Experimentation Tips & Analogies (Pizza Dough)
-  startOffset: 3772
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=3772
-  endOffset: 3839
-- name: Hiring, Resources & Contact Information
-  startOffset: 3839
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=3839
-  endOffset: 3880
-- name: Episode Wrap-up and Key Takeaways
-  startOffset: 3880
-  url: https://www.youtube.com/watch?v=0Gqx1LtqRZU&t=3880
-  endOffset: 3817
 ---