Timeline for When a one-tailed test passes but a two-tailed test does not
Current License: CC BY-SA 3.0
15 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 30, 2017 at 16:39 | vote | accept | monk | ||
| Aug 19, 2017 at 12:19 | history | tweeted | twitter.com/StackStats/status/898882054905057280 | ||
| Aug 19, 2017 at 6:53 | answer | added | Björn | timeline score: 1 | |
| Aug 19, 2017 at 0:22 | comment | added | Glen_b | @monk Please avoid the word "passes" (as in the title) as it's not clear whether that's intended to be "rejects the null" or "fails to reject the null" | |
| Aug 18, 2017 at 20:51 | comment | added | whuber♦ | This is understandable to US citizens, who at a very early age are taught the distinction between "innocent until proven guilty" and "presumed guilty until shown innocent." It is clear even to children (and I don't mean to cast any aspersions in saying so) that in one situation a person will not be convicted but in the other they can be, even when the evidence and the arguments in both cases are identical. This is so close in spirit to how statistical testing works that it is often invoked as an analogy. Perhaps, as an analogy, it could help your journalist craft an explanation. | |
| Aug 18, 2017 at 20:09 | history | edited | monk | CC BY-SA 3.0 | added 40 characters in body |
| Aug 18, 2017 at 20:07 | comment | added | monk | Thanks. Perhaps my question is a more sociological one. I edited my question to be clear about the weird part: a journalist (or other layperson) seems to be able to conclude "studies [on the same data] show that heights have not changed, but have gone up." | |
| Aug 18, 2017 at 20:02 | history | edited | monk | CC BY-SA 3.0 | added 4 characters in body |
| Aug 18, 2017 at 19:29 | answer | added | Alecos Papadopoulos | timeline score: 5 | |
| Aug 18, 2017 at 19:20 | answer | added | user83346 | timeline score: 2 | |
| Aug 18, 2017 at 19:09 | comment | added | whuber♦ | Researcher 2 is making a different assumption and has a different objective than Researcher 1. Contrary to the impression given by @KenS (who very well might be correct in general about statistics being unintuitive), it seems perfectly sensible to me--and intuitively obvious--that the two could (and, on occasion, should) arrive at different conclusions in the same circumstances. It would be weird if some theory required both of them always to draw the same conclusions from the same data--that ought to lead us to suspect the theory was flawed. | |
| Aug 18, 2017 at 18:59 | answer | added | Aksakal | timeline score: 2 | |
| Aug 18, 2017 at 18:42 | comment | added | Joel W. | Your example supports the use of effect sizes to understand or interpret your findings. Personally, I find both effect sizes and statistical significance useful. | |
| Aug 18, 2017 at 17:16 | comment | added | KenHBS | You're correct. Statistics is weird like that. Many things have to be derived very carefully and mathematically, and at the end of the day the conclusion you draw is never truly black or white. | |
| Aug 18, 2017 at 16:52 | history | asked | monk | CC BY-SA 3.0 |