LivePerson Developers is proud to host a meetup about A/B testing by Shlomo Lahav, Chief Scientist at LivePerson. The lecture will focus on testing and the ability to deduct conclusions, especially in the web. - What is an A/B test? - How to construct an A/B test properly? - What are the metrics that can be used? - Can the results be miss leading? - Errors: bias and statistical errors - First and second type errors - Measuring lift, why lift is a biased measure. - Is it possible to change the test settings during the test? - How to run a multivariate testing effectively?

1. A/B testing
Shlomo Lahav

2. The problem
Measuring the effect of multiple alternatives
on the performance over a given population.
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3. Performance
A list of objective measurements
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4. Possible solutions
• A model that describe the results and
evaluates the marginal effect of the
alternatives
• Test the alternatives side by side while all
the rest is equal
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5. Example
• the problem: Testing two different layouts
of a web page (A and B)
•
•
•
•
Population: visitors/visits
Performance: conversion rate
Alternatives: two different layouts
Objective: the find the better layout and
asses the performance difference
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6. What does it mean all the rest being equal
• Fairness: for every member in the
population, the probability to be allocated
to A is the same.
• For each member, any other decisions is
independent with the test allocation (A/B).
• Observations are independent
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7. Population: Visitor vs. visit
Population
Visitor
Visitor
Visit
Measurement
Visit conversion
rate
Lifetime
conversions per
visitor
Visit conversion
rate
Issues
Independency is
violated
A visitor may be
exposed to both A
and B (in different
visits)
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8. Errors
• When we compare a test alternative to the
control alternative
• False Positive – Calling the test to be the
winner by mistake
• False Negative – calling the control to be
the winner by mistake
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9. When do we end the test
• After a predefined period/observations.
• When the difference is significant
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10. What does it mean all the rest being equal
• Fairness: for every member in the
population, the probability to be allocated
to A is the same.
• For each member, any other decisions is
independent with the test allocation (A/B).
• Observations are independent
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11. Example
• We want to test two alternatives and
select the better one.
• The results are: CR(A)=9.21%,
CR(B)=11.93%. The win of B is statistical
significant (p-value<5%).
• We need to estimate the gain of B vs. A.
• Is our estimate of 2.72% a fair estimate?
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12. Results
p-value
Rate
Actual
A
B
Gain B
over A
10.00%
11.00%
1.00%
B wins
5%
92.5%
9.21%
11.93%
2.72%
A wins
5%
7.5%
13.71%
7.61%
-6.10%
B wins
1%
98.5%
9.59%
11.43%
1.84%
A wins
1%
1.5%
14.94%
7.05%
-7.89%
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13. Selection bias
• An AB test is conducted between A1,
A2,…,An
• After the test is completed, we select Ak.
• Should we expect Ak to perform as it did
during the test?
• Does the test outcome (the rank of k)
affects our expectation?
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14. What else can go wrong?
• Independency is not maintained (traffic,
changes etc.)
• The fairness is handled by random
allocation. This can be biased due chance
• The significance level is usually higher
than planned (continues evaluation) which
results in a higher false positive.
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15. How to control the traffic split?
• By percentage or round robin?
• Can we change the split?
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16. Another example
• Need to test two design layouts in multiple
location, while each location has a
different conversion rate.
• Different populations – use lifts and
accumulate the lifts.
• How do we calculate the lift: A over B or B
over A?
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17. lifts
A
B
8%
10%
10%
8%
Average
Lift B over A Lift A over B
25%
-20%
-20%
25%
2.5%
-2.5%
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18. Change in split - Simpson ‘s paradox
New
Returning
A
B
CR(A)
CR(B)
CR(A)
6%
15%
CR(B)
5%
14%
Weekday
80%
20%
90%
10%
7.80%
6.80%
Weekend
10%
90%
50%
50%
14.10%
13.10%
10.05%
12.05%
total
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19. Can we remove alternatives
• Start with 3 alternatives (equal split)
• Remove one
start
0
0
0.5
0.5
1
1
modify
0
0
0
1
1
1
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20. Multiple tests
• Is it valid to run multiple AB tests
simultaneously?
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