Saturdata

What if your A/B test needed 67 years to reach statistical significance? Sam found out the hard way. Join Sam and Shifra as they demystify statistical testing for the real world of data work, where the stakes are lower, the data is messier, and your stakeholders definitely do not know what a p-value is.

We talk about:

• P-values, null hypotheses, and why 0.05 was basically made up
• Type 1 and type 2 errors through the lens of job interviews
• When A/B testing actually makes sense (hint: you need more than 10 visitors a day)
• T-tests, chi-square, ANOVA, and F1 scores explained without the jargon
• Why a suspiciously high model accuracy is actually a red flag
• The difference between statistical significance and practical significance

Chapters:

0:00 - The 67-year A/B test

0:22 - Welcome to everyone's favorite hobby

1:37 - Knowing how to interpret tests (not run them)

2:27 - Is the analysis actually important to the business?

3:37 - P-values refresher: what they are and aren't telling you

6:07 - Why a raw p-value isn't enough

7:40 - Null vs. alternative hypotheses explained

10:16 - Type one and type two errors (a.k.a. the costly mix-ups)

15:06 - Lift: measuring if your marketing actually did anything

18:53 - When you already have all the data, statistics isn't the tool

20:57 - Sample size, statistical significance, and the 67-year problem revisited

24:04 - Common A/B test types: t-tests, chi-square, and ANOVAs

26:44 - F1 scores, confusion matrices, and picking the right metric

29:19 - Central limit theorem and the magic number 30

31:31 - We never prove things — we just reject the null

34:51 - Premortems and deciding if a project is even worth doing

35:52 - When n is too small vs. too big (and why both are a problem)

38:00 - Effect size: the stat that doesn't care how big your sample is

41:39 - Regression, slope, and explaining it to real humans

47:07 - Spend your time on the right things, not the fanciest model

52:33 - Wrap-up and big takeaways

Ever wonder how companies figure out which customers actually matter most? Sam breaks down how lift models rank your entire customer base by purchase probability so you can zero in on the top 20% driving 80% of your revenue.

Stop spreading your efforts thin and start focusing where it counts!

#reels #saturdata #data #liftmodel #datascience #analytics #8020rule

Shifra's favorite AP Stats memory? A free response question that assumed every student would casually verify study results at home, complete with test stats and standard deviations.

Clearly written by armchair professors who thought high schoolers would just kick back and double-check the math for fun. Spoiler: nobody's doing that 😂

#reels #saturdata #data #statistics #APstats #mathhumor #studentlife

Sam keeps it real: the craziest model with the best significance won't get you recognized.

What will? Delivering fast, communicating clearly, and understanding what actually moves the needle for the business.

The "soft" skills you think don't matter? They're the whole game. 🎯

#reels #saturdata #data #datascience #careertips #softskills #analytics

Your result might be statistically significant, but does it actually matter? Shifra breaks down why effect size (hello, Cohen's D!) is the real MVP. Something can be significant at the scale of nanometers and still be a total snooze.

Don't skip the "how much do we care?" step! 📏

#reels #saturdata #data #statistics #effectsize #datascience #pvalue

In school, they hand you the X and Y on a silver platter. But in the real world? Figuring out what to plot is the actual challenge. Sam breaks down why choosing the right axes is the heart of any good data analysis project. 📊

#reels #saturdata #data #analytics #datascience #slope

If you can't answer "what does each row represent?" you don't actually understand your dataset. Shifra breaks down why granularity is the foundation of every good data model, from orders to items to customers.

Before you build that stakeholder dashboard, make sure you know your primary key! 🔑

#reels #saturdata #data #datamodeling #dataquality #analytics #dataengineering

Think you know SQL? Sam and Shifra break down what separates a query writer from a true data thinker, from basic selects all the way to distributed systems, query plans, and the four pillars of production-ready code. Plus: why your data provider's incentives are working against you, how a 1,400-line monolith hid millions in overstated revenue, and the one approach that will save you from silent, soul-crushing data failures.

🌊 Check out the deep dive here: https://youtu.be/ayNKmcIELEo

We talk about:

- Three levels of SQL (and a secret fourth)

- What they don't teach you in school: data fluency, granularity, and audit patterns

- Why SELECT DISTINCT and ORDER BY are secretly expensive

- WORM vs. WARO: taking cost at write time vs. read time

- Idempotency, query plans, and writing SQL like a query engine

Follow Saturdata, your favorite weekend data podcast:

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Instagram: @SaturdataPod

#Saturdata #SQL #DataEngineering #DataAnalytics #QueryOptimization

Chapters:

0:00 - Intro: Squeal and Sasquatch

2:34 - The three levels of SQL: Basic, intermediate, and advanced

6:47 - When advanced SQL isn't really SQL anymore

9:12 - The 1,400-line monolith horror story

13:00 - The four "ilities": Readability, maintainability, observability, and explainability

15:34 - Write once, read many: Taking the cost at write time vs. read time

18:04 - SQL as analyst vs. SQL as architect

22:52 - Platform-dependent code and why your cloud provider is not your friend

25:31 - What school never taught you: Data fluency, granularity, and knowing your tables

35:09 - Why all of this matters: Defending your query like a lawyer

41:49 - What's holding people back: Select star, distinct, and the monolith trap

46:30 - Idempotency, query plans, and thinking like an engine

Watching a concept click is one thing, but wrestling with real errors until something finally works is what makes it actually stick.

Shifra breaks down why building projects with minimal AI involvement trains your brain in a way no tutorial ever can. School gives you the concept then the problem. The real world flips it, and that flip changes everything. 🧠💡

#reels #saturdata #data #learning #datacareer #techeducation #growthmindset

Unused tables cluttering your database aren't just a cost problem, they're a focus problem.

Sam explains how regular data audits can eliminate mental noise and free your team to think about what actually matters. A little cleanup goes a long way! 🧹✨

#reels #saturdata #data #dataengineering #dataops #productivity #DataTeams