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Take Action on Results With Statisitcs An Optimizely Online Workshop Statistician: Leonid Pekelis

Optimizely’s Stats Engine is designed to work with you, not against you, to provide results which are reliable and accurate, without requiring statistical training. At the same time, by knowing some statistics of your own, you can tune Stats Engine to get the most performance for your unique needs.

After this workshop, you should be able to answer… 1. Which two A/B Testing pitfalls inﬂate error rates when using classical statistics, and are avoided with Stats Engine? 2. What are the three tradeoﬀs in an A/B Test? And how are they related? 3. How can you use Optimizely’s results page to best tune the tradeoﬀs to achieve your experimentation goals?

We will also preview How to choose the number of goals and variations for your experiment.

First, some vocabulary (yay!)

• A) The original, or baseline version of content that you are testing through a variation. • B) Metric used to measure impact of control and variation • C) The control group’s expected conversion rate. • D) The relative percentage diﬀerence of your variation from baseline. • E) The number of visitors in your test. Which is the Improvement?

• A) Control and Variation The original, or baseline version of content that you are testing through a variation. • B) Goal Metric used to measure impact of control and variation • C) Baseline conversion rate The control group’s expected conversion rate. • D) Improvement The relative percentage diﬀerence of your variation from baseline. • E) Sample size The number of visitors in your test.

Stats Engine corrects the pitfalls of A/B Testing with classical statistics.

A procedure for classical statistics (a.k.a. “T-test”, a.k.a. “Traditional Frequentist”, a.k.a “Fixed Horizon Testing” ) Farmer Fred wants to compare the eﬀect of two fertilizers on crop yield. 1. Chooses how many plots to use (sample size). 2. Waits for a crop cycle, collects data once at the end. 3. Asks “What are the chances I’d have gotten these results if there was no diﬀerence between the fertilizers?” (a.k.a. p-value) If p-value < 5%, his results are signiﬁcant. 4. Goes on, maybe to test irrigation methods.

Classical statistics were designed for an offline world. 1915 Data is expensive. Data is slow. Practitioners are trained. 2015 Data is cheap. Data is real-time. Practitioners are everyone.

The modern A/B Testing procedure is different 1. Start without good estimate of sample size. 2. Check results early and often. Estimate ROI as quickly as possible. 3. Ask “How likely did my testing procedure give a wrong answer?” 4. Many variations on multiple goals, not just 1. 5. Iterate. Iterate. Iterate.

Pitfall 1. Peeking

Peeking Time Experiment Starts p-Value > 5%. Inconclusive. Min Sample Size p-Value > 5%. Inconclusive. p-Value > 5%. Inconclusive. p-Value < 5%. Signiﬁcant!

Why is this a problem? There is a ~5% chance of false positive each time you peek.

Peeking Time Experiment Starts p-Value > 5%. Inconclusive. Min Sample Size p-Value > 5%. Inconclusive. p-Value > 5%. Inconclusive. p-Value < 5%. Signiﬁcant! 4 peeks —> ~18% chance of seeing a false positive

Pitfall 2. Mistaking “False Positive Rate” for “Chance of a wrong conclusion”

Say I run an experiment.

1 original page, 5 variations, 6 goals = 30 “A/B Tests”

After I reach my minimum sample size, I stop the experiment and see 2 of my variations beating control and 1 variation losing to control

Winner Winner Loser Classical statistics guarantee <= 5% false positives. What % of my 2 winners and 1 loser do I expect to be false positives?

Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Loser Inconclusive Inconclusive Winner Inconclusive Inconclusive Winner Inconclusive Inconclusive Inconclusive 2 winners, 1 loser, and 27 inconclusives

Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Inconclusive Loser Inconclusive Inconclusive Winner Inconclusive Inconclusive Winner Inconclusive Inconclusive Inconclusive 30 A/B Tests x 5% = 1.5 false positives!

Classical statistics guarantee <= 5% false positives. Winner Winner Loser What % of my winners & losers do I expect to be false positives? Answer: C) With 30 A/B Tests, we can expect a 1.5 = 50% chance of a wrong conclusion! 3 In general, we can’t say without knowing how many other goals & variations were tested.

After this workshop you should be able to answer … 1. Which two A/B Testing pitfalls inﬂate error rates when using classical statistics, and are avoided with Stats Engine? 2. What are the three tradeoﬀs in an A/B Test? And how are they related? 3. How can you use Optimizely’s results page to best tune the tradeoﬀs to achieve your experimentation goals?

After this webinar, you should be able to answer … 1. Which two A/B Testing pitfalls inﬂate error rates when using classical statistics, and are avoided with Stats Engine? A. Peeking and mistaking “False Positive Rate” for “Chance of a wrong conclusion.”

The tradeoffs of A/B Testing

Error rates Runtime Improvement & Baseline CR

Error rates Runtime “Chance of a wrong conclusion” Improvement & Baseline CR

Error rates Runtime “Chance of a wrong conclusion calling a nonwinner a winner, or a nonloser a loser.” Improvement & Baseline CR

Error rates Runtime Improvement & Baseline CR

Where is the error rate on Optimizely’s results page? Statistical Signiﬁcance = “Chance of a right conclusion” = (a.k.a.) 100 x (1 - False Discovery Rate) I. II. III. IV.

How can you control the error rate?

Error rates Runtime Improvement & Baseline CR

Where is runtime on Optimizely’s results page?

Error rates Runtime Were you expecting a funny picture? Improvement & Baseline CR

Where is effect size on Optimizely’s results page?

These three quantities are all … Error rates Runtime Inversely Related Improvement & Baseline CR

Error rates At any number of visitors, Runtime Inversely Related the higher error rate I allow, the smaller improvement you can detect. Improvement & Baseline CR

Error rates Runtime Inversely Related At any error rate threshold, stopping your test earlier means you can only detect larger improvements. Improvement & Baseline CR

For any improvement, the lower error rate you want, the longer you need to run your test. Error rates Runtime Inversely Related Improvement & Baseline CR

What does this look like in practice? Baseline conversion rate = 10% Improvement (relative) Average Visitors needed to reach signiﬁcance with Stats Engine 5% 25% 95 (5%) Signiﬁcance Threshold (Error Rate) 10% 62 K 14 K 1,800 90 (10%) 59 K 12 K 1,700 80 (20%) 53 K 11 K 1,500

~ 1 K visitors per day Baseline conversion rate = 10% Improvement (relative) Average Visitors needed to reach signiﬁcance with Stats Engine 5% 25% 95 (5%) Signiﬁcance Threshold (Error Rate) 10% 62 K 14 K 1,800 90 (10%) 59 K 12 K 1,700 80 (20%) 53 K 11 K 1,500 (1 day)

~ 10K visitors per day Baseline conversion rate = 10% Improvement (relative) Average Visitors needed to reach signiﬁcance with Stats Engine 5% 25% 95 (5%) Signiﬁcance Threshold (Error Rate) 10% 62 K 14 K 1,800 90 (10%) 59 K 12 K 1,700 80 (20%) 53 K 11 K (1 day) 1,500

~ 50K visitors per day Baseline conversion rate = 10% Improvement (relative) Average Visitors needed to reach signiﬁcance with Stats Engine 3% 10% 95 (5%) Signiﬁcance Threshold (Error Rate) 5% 190 K 62 K 14 K 90 (10%) 180 K 59 K 12 K 80 (20%) 160 K 53 K (1 day) 11 K

> 100K visitors per day Baseline conversion rate = 10% Improvement (relative) Average Visitors needed to reach signiﬁcance with Stats Engine 3% 10% 95 (5%) Signiﬁcance Threshold (Error Rate) 5% 190 K 62 K 14 K 90 (10%) 180 K 59 K 12 K 80 (20%) 160 K (1 day) 53 K 11 K

After this workshop, you should be able to answer … 1. Which two A/B Testing pitfalls inﬂate error rates when using classical statistics, and are avoided with Stats Engine? 2. What are the three tradeoﬀs in an A/B Test? And how are they related? 3. How can you use Optimizely’s results page to best tune the tradeoﬀs to achieve your experimentation goals?

After this workshop, you should be able to answer … 1. Which two A/B Testing pitfalls inﬂate error rates when using classical statistics, and are avoided with Stats Engine? 2. What are the three tradeoﬀs in an A/B Test? And how are they related? A. Error Rates, Runtime, and Eﬀect Size. They are all inversely related.

Use tradeoffs to align your testing goals

In the beginning, we make an educated guess … ? Error rates Runtime 5% 53 K Inversely Related +5%, 10% Improvement & Baseline CR

… but after 1 day … Data! How can we update the tradeoffs?

1. Adjust your timeline

Improvement turns out to be better … Error rates Runtime 5% 1,600 Inversely Related +13%, 10% Improvement & Baseline CR Instead of: 53K - 10K = 43K

… or worse. Error rates Runtime 5% 75 K Inversely Related +2%, 8% Improvement & Baseline CR

2. Accept higher / lower error rate

Improvement turns out to be better … Error rates Runtime 1% 43 K Inversely Related +13%, 10% Improvement & Baseline CR

… or worse. Error rates Runtime 30% 43 K Inversely Related +2%, 8% Improvement & Baseline CR

3. Admit it. It’s inconclusive.

… or a lot worse. Error rates Runtime > 99% > 100K Inversely Related +.2%, 8% Improvement & Baseline CR iterate, iterate, iterate!

Seasonality & Time Variation Your experiments will not always have the same improvement over time. So, run A/B Tests for at least a business cycle appropriate for that test and your company.

After this workshop, you should be able to answer … 1. Which two A/B Testing pitfalls inﬂate error rates when using classical statistics, and are avoided with Stats Engine? 2. What are the three tradeoﬀs in an A/B Test? And how are they related? 3. How can you use Optimizely’s results page to best tune the tradeoﬀs to achieve your experimentation goals?

After this workshop, you should be able to answer … 1. Which two A/B Testing pitfalls inﬂate error rates when using classical statistics, and are avoided with for Stats Engine? 2. What are the three tradeoﬀs in one A/B Test? 3. How can you use Optimizely’s results page to best tune the tradeoﬀs to achieve your experimentation goals? A. Adjust your timeline. Accept higher / lower error rate. Admit an inconclusive result.

Review 1. Which two A/B Testing pitfalls inﬂate error rates when using classical statistics, and are avoided with Stats Engine? A. Peeking and mistaking “False Positive Rate” for “Chance of a Wrong Answer.” 2. What are the three tradeoﬀs in one A/B Test? B. Error Rates, Runtime, and Eﬀect Size. They are all negatively related. 3. How can you use Optimizely’s results page to best tune the tradeoﬀs to achieve your experimentation goals? C. Accept higher / lower error rate. Adjust your timeline. Admit an inconclusive result.

Preview: How many goals and variations should I use?

Stats Engine is more conservative when there are more goals that are not aﬀected by a variation. So, adding a lot of “random” goals will slow down your experiment.

Tips & Tricks for using Stats Engine with multiple goals and variations • Ask: Which goal is most important to me? -This should be the primary goal (not impacted by all other goals) • Run large, or large multivariate tests without fear of ﬁnding spurious results, but be prepared for the cost of exploration. • For maximum velocity, only test goals and variations that you believe will have highest impact.

Review 1. Which two A/B Testing pitfalls inﬂate error rates when using classical statistics, and are avoided with Stats Engine? A. Peeking and mistaking “False Positive Rate” for “Chance of a Wrong Answer.” 2. What are the three tradeoﬀs in one A/B Test? B. Error Rates, Runtime, and Eﬀect Size. They are all negatively related. 3. How can you use Optimizely’s results page to best tune the tradeoﬀs to achieve your experimentation goals? C. Accept higher / lower error rate. Adjust your timeline. Admit an inconclusive result.

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