#statstab #371 Safeguard Power as a Protection Against Imprecise Power Estimates
Thoughts: tl;dr - when replicating a study, use the lower end of the CI of the original study as your effect in a power analysis.
#statstab #371 Safeguard Power as a Protection Against Imprecise Power Estimates
Thoughts: tl;dr - when replicating a study, use the lower end of the CI of the original study as your effect in a power analysis.
New blog post
: Your Study Is Too Small (If You Care About Practically Significant Effects)
#effectsize #precision #poweranalysis #research #Psychology #MCID #SESOI #samplesize
#statstab #189 Post Hoc Power: Not Empowering, Just Misleading
Thoughts: "the observed power is a 1:1 function of the P value" If you need a ref for why "post hoc" power is nonsense (paywalled).
#power #posthoc #QRPs #errorcontrol #poweranalysis
https://www.sciencedirect.com/science/article/pii/S0022480420305023
Last week I attended the 6th Perspectives on Scientific Error Conference at @TUEindhoven
I learned so much! About #metascience #preregistration #replicability #qrp questionable research practices, methods to detect data fabrication, #peerreview, #poweranalysis artefacts in #ML machine learning...
I'm impressed by the commitment of participants to improve science through error detection & prevention. Thanks to the organizers Noah van Dongen, @lakens @annescheel Felipe Romero and @annaveer
Hello #statstodon! A reviewer asks me to perform a post-hoc #PowerAnalysis. I know this is generally not advised because if you replace the a priori effect size by the effect size measured in the experiment, this will introduce an erroneous relationship between the significance level of the test and the measured power.
… but does that mean that there is no proper way of measuring power retrospectively? For example, if you refrain from using the measured effect size and instead simulate a range of “a priori” effect size unrelated to the results of the test, then the dependency of the power to the significance level should not happen?
#stats #statschat @lakens
Online #workshop:
Simulation-based power analyses in (generalized) linear mixed models
17.05.2023, 10-12h CEST
The workshop will cover basics of power analysis, linear mixed models, and why the combination of both requires a simulation-based approach.
In my experience, this is for many areas of #HealthSciences and #HRQL research a key problem when designing studies.
Maybe worth a read as well:
https://link.springer.com/article/10.3758/s13428-021-01546-0
Can anyone point me to a paper/post explaining why you should not base your sample size calculation on the effect found in the literature or a pre-test? Pretty sure I read it once but cannot find it anymore. Maybe I saw it in @lakens's course? #poweranalysis #samplesize
I've seen posts about conducting power analyses by using pilot data to guide simulations/bootstraps. Does anyone know of any resources/papers/blog posts on how this works? #stats #rstats #PowerAnalysis #simulation