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Optimization & A/B Testing Statistics
Rating: 3.8 out of 5(195 ratings)
1,647 students

Optimization & A/B Testing Statistics

Speed up your a/b tests by doing it right from the start.
Created byJared Waxman
Last updated 11/2013
English

Course content

5 sections • 21 lectures • 2h 48m total length

  • Introduction1:54

    Madmen of today should be called mA/Bmen since they ply experimentation, not hard liquor from 9 to 5.

    Learn why and how to apply a/b testing to your marketing efforts.  We'll start at the beginning, with the foundations of the science behind hypothesis testing.

  • What Can It Do?3:32

    A quick intro what organizations gain from optimization programs and where it can be applied.

  • A/B Test Examples3:53

    We cover two different a/b tests to provide a starting point for what a test really looks like.

  • Key Terminology4:59

    We cover some of the special language of optimization.  A good reference to come back to.

  • Correlation & Causation10:17

    You probably know there is a difference.  But we apply this to optimization and explore the options.

Description

Whether you've got a lean startup or a fat Fortune 500, the faster you learn the faster you'll grow.  Optimization and a/b testing is at the heart of learning fast.

I guarantee you will learn something in this course that will raise your skill level.  With the 30-day money-back guarantee, you can't lose. 

We start with the basics, then cover the 8 steps of running a solid a/b test.  Next we dive deep into the statistics behind hypothesis testing.  In the long-run you will save your organization headaches by setting up tests correctly and analyzing them with the right statistical rigour. 

There is double and triple digit ROI around optimization for companies that figure it out.  Start now and impress your colleagues on Monday morning.

Topics include:

Examples of a/b tests

Hypothesis testing

Measurement as risk reduction

Selecting a KPI or success metric

8 Steps for Running an A/B Test

Selecting from amongs a/b test and MVT test designs

Lift Threshold

Null Hypothesis

Statistical significance

Sample size estimates

confidence interval

test statistic

t-tests

standard error of the mean

chi-square

Fischer Exact test

Statistical Power

Type I error

Type II error

p-values

How to choose what statistical test to run

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