Timeline for Test if population sizes are statistically different?
Current License: CC BY-SA 4.0
14 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 11, 2020 at 12:24 | vote | accept | Jedrek369 | ||
| Aug 10, 2020 at 13:26 | vote | accept | Jedrek369 | ||
| Aug 10, 2020 at 13:26 | |||||
| Aug 10, 2020 at 13:26 | vote | accept | Jedrek369 | ||
| Aug 10, 2020 at 13:26 | |||||
| Aug 7, 2020 at 10:46 | history | edited | Jedrek369 | CC BY-SA 4.0 |
Added more details and histograms.
|
| Aug 5, 2020 at 21:02 | answer | added | BruceET | timeline score: 1 | |
| Aug 5, 2020 at 13:34 | comment | added | Dave | Doveryai no proveryai...trust, but verify. | |
| Aug 5, 2020 at 13:25 | comment | added | Jedrek369 | @Björn Exactly, I want to be extra careful and check if the assignment was truly random. | |
| Aug 5, 2020 at 12:18 | comment | added | Björn | Why do you want to look at this (it really helps to be clear about the underlying question)? Are you trying to check whether the engineering team that implemented the A/B test messed up and due to their mistake the A/B test did not randomly assign visitors?! As @00schneider points out if this was properly randomly done, then there's no point in doing this. | |
| Aug 5, 2020 at 11:26 | comment | added | Dave | I have my own reservations about using paired t-testing, but what do you think about that? | |
| Aug 5, 2020 at 10:56 | comment | added | Jedrek369 | @Dave Yes, t-testing sounds right to me. The problem is I don't know how to use it to assess whether the sizes of the groups are statistically different. Can you show me how to do it or send me some sources to find out more on this subject? | |
| Aug 5, 2020 at 10:38 | comment | added | Dave | The trouble with what you propose is that “significance” aids is in drawing a conclusion about the population(s) from which the sample(s) were drawn. Sample size is not a property of the population; the population is the population whether you draw one sample, a bazillion-gazillion samples, or no samples. However, something feels “right” about examining if more people visit one website than the other. The naïve approach would be t-testing (probably paired) the number of visitors. Does that sound about like what you would do? Watch out for the time series nature of your data, however. | |
| Aug 5, 2020 at 10:10 | comment | added | 00schneider | If you randomized the allocation to control vs experimental group, you do not need to do a test because you already know that differences only can appear by chance. see for example this paper tandfonline.com/doi/abs/10.1080/00031305.2017.1322143 | |
| Aug 5, 2020 at 9:31 | review | First posts | |||
| Aug 5, 2020 at 10:18 | |||||
| Aug 5, 2020 at 9:30 | history | asked | Jedrek369 | CC BY-SA 4.0 |