Optimization & A/B Testing Statistics
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Try Udemy for BusinessWhether 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
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.
A quick intro what organizations gain from optimization programs and where it can be applied.
We cover two different a/b tests to provide a starting point for what a test really looks like.
We cover some of the special language of optimization. A good reference to come back to.