FAQ: Learn Sample Size Determination with SciPy - A/B Testing: Don't Interfere With Your Tests

This community-built FAQ covers the “A/B Testing: Don’t Interfere With Your Tests” exercise from the lesson “Learn Sample Size Determination with SciPy”.

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This exercise can be found in the following Codecademy content:

Data Science
Analyze Data with Python

FAQs on the exercise A/B Testing: Don’t Interfere With Your Tests

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Can someone explain to me why taking more samples past what the calculator tells you should be the minimum amount of samples is improper?

Take for example the coin toss they mentioned as an example. Sure if you are flipping a coin 10 times, then extending the trials to 15 or 20 could skew the results to look like one side of the coin favors the other. However, isn’t this dealing with very small numbers? Say instead you flip the coin 100 times? Should you increase it to 500? the 100 flips would most likely be close to 50/50 but 500 flips would be even more indicative of the true probability of the outcome (50/50). And isn’t that the whole point of A/B testing? To say with statistical confidence that this is the result? Isn’t that the point of having a minimum number of trials? So that adding anymore trials wouldn’t lend anymore significance due to not fluctuating the overall outcome? Referencing the coin flip again, if you add more tosses to 500 trials of a coin flip you wouldn’t expect it to fluctuate hardly at all. Why would adding any more trials matter?