Part IV - Terms - A/B Testing Flashcards

1
Q

When is A/B testing a good idea?

A

When you have clear measurable goals and are testing specific theories

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2
Q

When is A/B testing a bad idea?

A

When goals are vague and designs have multiple variables

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3
Q

How many variations should be in A/B testing?

A

Two at a time to maintain clarity in results

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4
Q

What is a null hypothesis in A/B testing?

A

The assumption that any difference in outcomes is due to chance

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5
Q

How many visits are needed for significant A/B test results?

A

Enough to reach a statistical significance of 95% confidence

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6
Q

What is multivariate testing?

A

Testing multiple variables to see how they interact over time

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7
Q

Does Google think negatively about A/B testing?

A

No; Google supports A/B testing as it does not count as duplicate content

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8
Q

How and when to interpret A/B testing results?

A

After reaching statistical significance to accurately reflect on the hypothesis tested

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9
Q

How many variables should be tested in A/B testing?

A

One at a time for clear actionable results

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10
Q

What should be tested in A/B testing?

A

Elements like CTAs, headlines, images, and copy length

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11
Q

Can A/B tests be run on non-webpage elements?

A

Yes, including emails, PPC campaigns, and CTAs

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12
Q

How to find A/B tests from similar companies?

A

Check websites like “Which Test Won” and “Visual Website Optimizer” for examples

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13
Q

What if A/B test results are not trusted?

A

Re-run the test to verify the results

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14
Q

How often should A/B testing be conducted?

A

Continuously as long as each test has a clear purpose

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15
Q

What is needed to start A/B testing on a site?

A

A/B testing software like HubSpot or Google Content Experiments

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16
Q

What are some validity traps/effects in A/B testing?

A

History effect and Instrumental effect, among others

17
Q

Should I A/B test my homepage?

A

Yes, but consider testing specific elements like CTAs due to variable visitor goals

18
Q

What if there is no control in an A/B test?

A

Make one; designate one variation as the control (typically the one most similar to current design)

19
Q

Why isn’t A/B testing split exactly 50/50?

A

Due to random variations (but it balances out with more traffic)