Like this presentation? Why not share!

# PyData London 2014 Martin Goodson- Most A/B Testing Results are Illusory

## on Mar 05, 2014

• 285 views

PyData London 2014 Martin Goodson - Most A/B Testing Results are Illusory

PyData London 2014 Martin Goodson - Most A/B Testing Results are Illusory

### Views

Total Views
285
Views on SlideShare
284
Embed Views
1

Likes
0
5
0

### 1 Embed1

 http://localhost 1

### Report content

• Comment goes here.
Are you sure you want to
Your message goes here

## PyData London 2014 Martin Goodson- Most A/B Testing Results are IllusoryPresentation Transcript

• Most A/B testing results are Illusory Martin Goodson, Skimlinks
• These are my opinions not those of my employer!
• What’s an A/B test? Example: Free delivery A: Control B: Variant
• ‘How can you talk for 40 minutes about A/B testing?’
• A/B tests are very easy to get wrong
• What my experience is based on
• What this talk is about 3 Statistical concepts Errors and consequences These errors are exactly how A/B testing software works
• What this talk is about Statistical Power Multiple Testing Regression to the Mean
• What is Statistical Power? The probability that you will detect a true difference between two samples
• What is Statistical Power? Example: are men taller than women, on average?
• What is Statistical Power? Example: free delivery on a website
• Why is Statistical Power important? 1. False negatives 2. False positives
• Precision Proportion of true positives in the positive results Its a function of power, significance level and prevalence.
• If you have good power? Out of 100 tests 10 really drive uplift You detect 8 5 false positives 8/13 of positive tests are real
• If you have bad power? Out of 100 tests 10 really drive uplift You detect 3 5 false positives 3/8 of winning tests are real!
• Marketer: ‘We need results in 2 weeks time’ Me: ‘We can’t run this test for only two weeks we won’t get robust results’
• Marketer: ‘We need results in 2 weeks time’ Me: ‘We can’t run this test for only two weeks we won’t get robust results’ Marketer: ‘Why are you being so negative?’
• Calculating Power Alpha: probability of a positive result when the null hypothesis is true (5%) Beta: probability of not seeing a positive result when the null hypothesis is true Power = 1- Beta (80-90%)
• Calculating Power Use a power calculator: Online R (power.prop.test) python (statsmodels.stats.power)
• Approximate sample sizes Using a power calculator and asking for 80% power and significance level of 5%: 6000 conversions to detect 5% uplift 1600 conversions to detect 10% uplift
• Multiple testing
• Effect of multiple testing if you run 20 tests at a significance level of 5% you will obtain 1 win, just by chance.
• Giving targets for successful tests.
• Stopping tests early
• Stopping tests early Simulations show that stopping an A/A test when you see a positive results will result in successful test 41% of the time.
• Stopping tests early That works out to a precision of 20%
• Negative uplift. Stopping an A/B test with negative effect results in a win 9% of the time!
• A True Story
• Regression to the mean Give 100 students a true/false test They all answer randomly Take only the top scoring 10% of the class Test them again What will the results be?
• Estimates of uplift are generally wrong.
• What you need to do to get it right ● Do a power calculation first to estimate sample size ● Use a valid hypothesis - don’t use a scattergun approach ● Do not stop the test early ● Perform a second ‘validation’ test
• My details martingoodson@gmail.com @martingoodson http://goo.gl/jvhwmB Download my whitepaper on A/B testing here
• Skimlinks After Party! Levante Bar 5 minutes away Come hungry! Invites + Map at the booth http://skimlinks.com/jobs