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We found the same pattern of results in another experiment, where we statistically controlled for people's math ability.
In another study, the researchers found the same pattern of results for a decidedly less glamorous food: the carrot.
The same pattern of results was evident in both intent-to-treat and responder analyses.
This was the same pattern of results found for the threat induction.
The premise of this test of replicability, of course, is that each of the labs should have generated the same pattern of results.
The same pattern of results was also obtained when discarding all NREM and REM RefRN targets presented around the slightest signs of awakening (Methods and Supplementary Fig. 4c).
Sensitivity analyses adjusting for number of sessions when assessing length of time per session and for participant characteristics showed the same pattern of results.
Sensitivity analyses for change in AUDIT score amongst responders-only when adjusting for app usage and participant characteristics showed the same pattern of results (Supplementary Table 6).
A sensitivity analysis with Bayes factors using a smaller expected effect size of a difference of 3 units showed the same pattern of results (Table 2).
When adjusting for app usage, there was the same pattern of results for 'ease of use'recommendationion' and 'satisfaction' though no main effect of Self-monitoring and Feedback on 'helpfulness' (F = 2.14, p = 0.15).
But we found the same pattern of results even for food so unpleasant that no one would view a supersized portion as a gain and a downsized portion as a loss.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com