Timeline for Most appropriate test for A/B testing results
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Jul 19, 2019 at 18:47 | comment | added | Erdal Dogan | No aggregated claims, no assumptions, no nothing :) They are precise numbers of visitors of a webpage. Thank you for your help. | |
| Jul 19, 2019 at 17:30 | comment | added | BruceET | Results should be OK if total visitor counts and conversion rates are both accurate. But if the claimed numbers of total visitors were due to bragging rather than counting, you could be in trouble. // Have to be careful these days: politicians claim 'millions and millions' when they actually talked to half a dozen supporters. | |
| Jul 19, 2019 at 17:21 | comment | added | Erdal Dogan | Instead of including the total numbers of visitors in the table, I used conversions and non-conversions(total_visits - conversion), since the visitors are directed the different versions by software and not equally distributed always. What I did was to calculate the expected conversions for each version ((total_conversions / total_visitors) * total_visits_on_version_x), then compared two 2x2 matrixes with an excel function (CHITEST). We have very close p-values though. I am not an expert on statistics, excuse my mistakes. | |
| Jul 19, 2019 at 17:17 | history | edited | BruceET | CC BY-SA 4.0 |
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| Jul 19, 2019 at 16:57 | history | edited | BruceET | CC BY-SA 4.0 |
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| Jul 19, 2019 at 16:52 | history | edited | BruceET | CC BY-SA 4.0 |
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| Jul 19, 2019 at 16:46 | history | answered | BruceET | CC BY-SA 4.0 |