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New paper: Blaming the Thermometer for the Fever: Separating Misapplication from Method in Null Hypothesis Significance Testing, with @laabho and @jesper_wulff . We - strongly - push back on criticisms against NHST, and argue for its proper application. https://t.co/RtIlZJUHx4 >
@matloff@lakens@jesper_wulff I see where you are coming from, but the binary structure of NHST is a feature, not a bug. It does not ignore uncertainty... it manages it through predefined error control. CIs serve a different role in summarizing estimation uncertainty, not guiding decisions.
New preprint with @lakens and @jesper_wulff:
"Blaming the Thermometer for the Fever: Separating Misapplication from Method in NHST"
NHST gets a lot of blame—mostly for issues it did not cause!
Read it here: https://t.co/b6eXD0cu8W
@matloff@lakens@jesper_wulff (a) Measurement error affects all estimates.
(b) A p-value is a probability statement, not a decision rule. It only “usurps” the analyst role if allowed.
But you should read the paper; we do not claim NHST as the only useful methodology
As promised, the preprint is now out:
"Blaming the Thermometer for the Fever: Separating Misapplication from Method in NHST"
We push back against calls to ban dichotomous testing and defend error control—properly applied.
Read here: https://t.co/b6eXD0d1Yu
We will soon post the corresponding preprint 'Blaming the Thermometer for the Fever: Separating Misapplication from Method in Null Hypothesis Significance Testing' which pushes back on the idea to ban error control in science by not allowing dichotomous tests.
@Afinetheorem Good thread! No need to manually maintain a .bib file—use a reference manager like Zotero. Just add a DOI or ISBN, and it will fetch all the right details. You can share reference lists with coauthors, and it integrates seamlessly with Overleaf
@intuitidbits Is this really what anyone would consider an acceptable DAG? I see absolutely no convincing argument here directly linking low RHR to aggression… I do see copious amounts of selective reporting though
@RexDouglass But using smallest effect size of interest to test against clearly shows that it is not a made up concept to sell power analysis… clearly it serves other practical purposes
@RexDouglass To be fair, any concept in statistics is made up. Besides, smallest effect size of interest is useful for other things than power analysis… for example to test against instead of null thus providing a more severe test
@CharlesDriverAU@lakens@J_A_Quent But why would you want indirect error control when alpha offers a direct, and as you point out, standardized way of achieving this?
@RexDouglass Gelman aside, it seems to me that the three main points he makes are completely fair and not mischaracterized, shallow or creepy as you postulate. In fact this very blog is about dichotomous choices.
@andrewgwils@lukaemon Quite the conjecture… if used to point out flaws, discrepancies, grammar mistakes, or propose better structure, rather than simply creating content, wouldn’t it only make you a better writer in the long run?