『Saturdata』のカバーアート

Saturdata

Saturdata

著者: Saturdata Podcast
無料で聴く

概要

Join Shifra Williams and Sam LaFell as they discuss anything and everything related to data.

This is your podcast if you are looking from advice from people "one step ahead", and you are somewhere in the first 0-5 years of your data career.

Anyone that's more seasoned could also benefit from seeing the perspectives of those currently in that position or just past those positions.

Shifra Williams, Sam LaFell, 2025
出世 就職活動 経済学
エピソード
  • Statistics 101 at work | Saturdata
    2026/03/15

    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

    続きを読む 一部表示
    53 分
  • Inconsistent colors are sabotaging your charts
    2026/03/16

    You know what's worse than color-coding your data groups?

    Switching the colors between charts. Shifra breaks down how inconsistent color use creates a false "design language" that misleads your audience. If group A is yellow in one chart, it better be yellow in the next one too! 🎨

    #shorts #saturdata #data #dataviz #charttips #designlanguage #datavisualization

    続きを読む 一部表示
    1 分
  • "Stop chasing every customer, your top 20% are doing all the heavy lifting "
    2026/03/13

    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

    続きを読む 一部表示
    1 分
まだレビューはありません