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A/B Testing Got You Elected Mister President
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Penelope Phippen
April 06, 2013
Technology
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A/B Testing Got You Elected Mister President
Penelope Phippen
April 06, 2013
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Transcript
A/B Testing Got you elected Mister President
@samphippen @samphippen
Should I make this change?
Users A group: 50% B group: 50% Site change Old
site
Measure some metric
Do maths on the two groups
???
Profit
Lemme show you my favourite A/B test
None
None
None
None
None
None
Also some videos
None
+$60 million
None
Protips
Same user always sees same version
Caching
Roughly same performance
Also for feature flagging
A super lightning fast guide on how to do it
and what it looks like
gem 'split'
require 'split/dashboard' run Rack::URLMap.new \ "/" => YourApp::Application, "/split" =>
Split::Dashboard.new
<% ab_test("experiment_name", "a", "b") do |c| %> <a href="/win" class="btn
<%= c %>"> Get points? </a> <% end %>
What it looks like
None
None
None
https://github.com/ andrew/split
How to interpret the results
Stats time
Confidence Value
P =0.95 is used in medical trials
Common mistake: Assumption of normality
None
This will probably work for you
How to design the experiment
Step 1: clearly state your hypothesis
Example: I will get more donations if our button is
jimmy wale’s face
Formally: Null Hypothesis: there will be no increase in donations
if we use jimmy wales face
Formally: positive Hypothesis: there will be an increase in donations
if we use jimmy wales face
Step 2: Pick a statistical test
Example: difference of proportions (the standard A/b test)
http://stattrek.com/ hypothesis-test/ difference-in- proportions.aspx
Step 3: Decide an experiment length (number of days)
Example: we get 200 hits a day, let’s test for
15 days for 3000 hits
Alternatively: A fixed sample size Stop after 10000 users
Step 4: Split
Half the users get jimmy wales face half the users
get whatever the button was before
Step 5: inspect results and analyse
Let’s talk about analysis
Let’s work two examples (one null, one positive)
With jimmy Without Jimmy Users in test 100 100 Users
that clicked 27 18
Confidence = 93.6% Too low at 95% to conclude that
this is better
common mistake: Sample size
With jimmy Without Jimmy Users in test 1000 1000 Users
that clicked 270 180
99.9% confidence High enough for us to declare this better
Confounding factors ARE bad
this is hard stuff I hope you understood :) ask
me questions @samphippen