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  1. If I were to say that the primary benefit of randomization (and possibly blinding) is that it makes simple statistical models match the true data generating process quite well, would that be a provocative statement? Or would that be obvious? Is there a good reference for this line of reasoning?

    #stats #statstodon #phil #philosophy #philsci #philscidon

  2. Great guide to thinking about the age-period-cohort problem by Julia Rohrer: osf.io/preprints/psyarxiv/8zmu

    #stats #statstodon

  3. 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

  4. I have long-term issues getting planned missingness right.
    Always seem to not be enough for the sample size for FIML or multiple imputation.

    End up with missing covariance elements & nasty error messages.

    Any good resources on figuring out %missing & N for planned missingness?

    #stats #statstodon

  5. @philipncohen @WeedenKim

    It seems like every picture from the strike has multimillionaire in the center?

    I mean, the distribution of compensation between actors and movie&tv executives seems very similar. Top 10 actors get the same as top ten movie executives (~ $400 million) and then it starts to decrease but familiar actors still get the same as execs just below C-suite.

    #SAG #statstodon

  6. @DToher

    #Statstodon is apparently a thing. They might want in on that. 😀