Is “Statistical Significance” Outdated?
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- This topic has 4 replies, 4 voices, and was last updated 1 year, 5 months ago by
Chris Seider.
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October 13, 2020 at 10:54 am #250439
Fausto GalettoParticipant@fausto.galettoInclude @fausto.galetto in your post and this person will
be notified via email.In some papers downloaded in October 2020 I found the following:
- 1. We conclude, based on our review of the articles in this special issue and the broader literature, that it is time to stop using the term “statistically significant” entirely.
- 2. Nor should variants such as “significantly different,” “p < 0.05,” and “nonsignificant” survive, whether expressed in words, by asterisks in a table, or in some other way.
- 3. Regardless of whether it was ever useful, a declaration of “statistical significance” has today become meaningless.
The special issue was by The American Statistician.
I do not agree with these statements.
Do some of the colleagues can make some comments?
Thanks
0October 17, 2020 at 12:57 pm #250499
Chris SeiderParticipant@cseiderInclude @cseider in your post and this person will
be notified via email.No.
Howerver, I’d suggest you consider attaching a pdf or link or web address for link. Hard to see what you are generating buzz about.
1December 7, 2020 at 7:11 am #251244
David007Participant@David007Include @David007 in your post and this person will
be notified via email.I’m familiar with the paper and, for the first time, agree with you Fausto. There are real concerns about the misuse of p-values, but that doesn’t mean we should stop using them.
0January 4, 2021 at 7:39 am #251640
tgauseParticipant@chebetzInclude @chebetz in your post and this person will
be notified via email.@cseider: Hi, Chris! Try this: https://www.tandfonline.com/doi/full/10.1080/00031305.2019.1583913
0January 5, 2021 at 8:55 pm #251663
Chris SeiderParticipant@cseiderInclude @cseider in your post and this person will
be notified via email.Thanks @chebetz
Interesting article. It’s kind of “wonky” but they seem to be making a mountain out of a mole hill if I have the expression correctly. Basically, just because one picks the “correct” side of the stat test with the appropriate p-value, there is uncertainty which is why things should always be proven “in the field” or else one wouldn’t get past the Improve/Control phases!
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