Timeline for Why Student's t-test doesn't require normality of population?
Current License: CC BY-SA 4.0
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| Jan 29, 2022 at 19:47 | comment | added | lambda | That’s true @glen_b; dividing the scenarios into industry and academia is too coarse. | |
| Jan 29, 2022 at 11:48 | history | edited | kjetil b halvorsen♦ | CC BY-SA 4.0 |
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| Jan 29, 2022 at 6:17 | comment | added | Glen_b | ... So how do they know that they're not in one of those "the approximation very poor" situations? People weirdly write "academic" as if they imagined that academics never solve real problems when many do regularly. I have been an academic, but I work in industry and have done so in at least some capacity continually for more than 30 years. Being in industry does not mean you can't be careful, or thoughtful. It doesn't mean you can't make sure your claims hold water. It doesn't mean you can't do some actual checking that things have the properties you want them to. | |
| Jan 29, 2022 at 6:15 | history | edited | Glen_b | CC BY-SA 4.0 |
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| Jan 29, 2022 at 6:12 | comment | added | Glen_b | They say that a lot ... but you have to ask is that actually the case? (The answer is not if they want a t-statistic to actually have a t-distribution -- note that a t-statistic is not just a numerator. If you ignore that, at best we're dealing in approximations, and in that case how do we tell if it's a good approximation? We could do simulations like the above ... where we have already seen that sometimes the answer is that the approximation is very poor) | |
| Jan 29, 2022 at 6:02 | comment | added | lambda | Thank you @glen_b for the excellent counter example. I don’t know about the backgrounds of the discussion participants in this thread, but I do see everybody is stressing that we need to be cautious about over-optimistism of the normality violation. I don’t know if this is just a difference in industry vs. academia. I do see A/B testing practitioners in industry embrace the no-normality-is-ok more enthusiastically. Some books even say: no normality is OK, because the normality assumption is only on the sampling distribution of $\bar{Y}$,not on the ${Y_{i}}$. | |
| Jan 27, 2022 at 4:16 | history | edited | Glen_b | CC BY-SA 4.0 |
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| Jan 25, 2022 at 11:47 | history | edited | Glen_b | CC BY-SA 4.0 |
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| Jan 25, 2022 at 11:41 | history | edited | Glen_b | CC BY-SA 4.0 |
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| Jan 25, 2022 at 11:30 | history | edited | Glen_b | CC BY-SA 4.0 |
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| Jan 25, 2022 at 11:24 | history | edited | Glen_b | CC BY-SA 4.0 |
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| Jan 25, 2022 at 11:16 | history | edited | Glen_b | CC BY-SA 4.0 |
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| Jan 25, 2022 at 10:53 | history | answered | Glen_b | CC BY-SA 4.0 |