FAQ: A/B Testing: Sample Size Calculators - Don't Interfere With Your Tests

Frequently Asked Questions Python FAQ
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This community-built FAQ covers the “Don’t Interfere With Your Tests” exercise from the lesson “A/B Testing: Sample Size Calculators”.

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This exercise can be found in the following Codecademy content:

Master Statistics with Python

FAQs on the exercise Don’t Interfere With Your Tests

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Would repeating the same A/B test at another time be considered the same type of interference as extending the sample size? I can imagine being asked to repeat the experiment until the desired significance is observed.

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But it wouldn’t be the exact same A/B test because the groups would be comprised of different users, or user ids or whatever groups you’re testing for a potential difference.

This is not a clear understanding of why A/B tests are run. This would be a false reading of the data and a manufactured outcome with results are not an accurate representation of the data collected.

It would be helpful to read up more about A/B testing, reasoning behind it, it’s role, and some common pitfalls.

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These rainbow fingerling potatoes don’t come cheap. We’d like to know for sure that, with this change, we’ll be pulling in at least $1240 more every week. In order to figure out how many more customers we need, we’ll have to investigate the average revenue generated from a given sale. Luckily we have a list of the money spent by each customer in a typical week: noshmishmosh.money_spent . Save that list into a variable called payment_history .

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Could you please explain this part? I can’t understand it at all?
Also, I search I couldn’t find a better place to send this. This is for “A/B Testing at Nosh Mish Mosh” on business analytics track.

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To be able to make a purchase of rainbow fingerling potatoes, they need at least $1240 more every week. To get that much, they need to figure out how many more customers would amount to $1240+ every week. This is why the changes they make on the site should be workable.