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  1. Pinned Tweet

    My new MOOC "Improving Your Statistical Questions" has launched! . There are 15 videos and 13 assignments, all freely available. I hope you'll like it! An overview of the contents and a thank you to all who helped in this blog post:

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  2. Retweeted
    Replying to

    Yes. I am particularly interested in the trade-off between writing the paper you are able to write now, and investing time to learn how to write the paper you currently can't.

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  3. This tweet was partly inspired by a coffee machine chat with the head of another department when he was about to retire. He said he looked forward to having time to read and think. I think most people wouldn't expect scientists feel they need to retire to have time for this.

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  4. Retweeted
    8 hours ago

    Paper👇🏻 by Baumeister makes some bold claims about what we can(‘t) learn from effect sizes. wrote an insightful response that also happens to be his (signed!) referee report from when the paper was under review elsewhere Read here:

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  5. If you are a scientist, do you feel you have enough time in your job to learn new skills you think would improve the way you do science? If a magic wand could free up time to learn new skills that improve the science you are doing, how much time would you like to have?

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  6. I like a transparency based ranking system. Let this measure become a target. Many of these measures (data sharing, publishing replication studies) are actually just the basics of doing solid science.* *I do have a small CoI. You might guess which journal will do best.

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  7. Perhaps my comments are interesting, perhaps not. Regardless, it would be nice if all the time researchers put into reviews is not wasted, and their thoughts can be made known. So far, is the only journal I know that publishes reviews of rejected manuscripts.

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  8. New blog post, consisting of the review I submitted for this article a bit more than a month ago: Article was rejected, but is now accepted elsewhere without substantial changes. I thought the article was relatively weak.

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  9. "Even today, many philosophers criticize the strawman Popper_0." "The real Popper developed from dogmatic to a naive version of methodological falsificationism in the twenties; he arrived at the 'acceptance rules' of sophisticated falsificationism in the fifties." Lakatos, 1978

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  10. Nice example of the impact psychological science has on society. Such an important and difficult to study research topic.

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  11. Retweeted
    Jan 31

    NEW TALK! I walk through *why* it’s important to have equivalence tests & Bayesian hypothesis tests in your statistical toolkit and *how* to perform these tests in and . Full talk [50 mins] Slides Preview ⤵️

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  12. Retweeted
    Jan 31

    Teaching qualitative methods? Come work with us in Amsterdam!

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  13. "Scientific hypotheses and theories are not derived from observed facts, but invented in order to account for them." Hempel, 1966

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  14. Retweeted
    Jan 30

    I've just read this preprint on machine-readable hypotheses by and : . What they propose is very precise specification not only of hypotheses as such but also of how to decide when the hypotheses are corroborated. (1/4)

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  15. Retweeted

    What I'm planning for the summer: The Statistics Wars & Their Casualties. Interested? (rough 1st draft)

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  16. OK, this was a joke but a body cam is 150 euro and now I just want to put one on the experimenter in our human-technology interaction lab who when asked just says "it is essential for the experiment that we are fully transparant, please continue".

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  17. A surprisingly large number of these frustrating claims would go away if people would just correct their alpha level for multiple comparisons as taught in intro stats. A handful of statisticians argue against these corrections. Their papers are cited 1000s of times. Go figure ;)

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  18. The problem is often referred to as 'crud' (for some explanation, see ). It is not easy to overcome. Providing empirical support for your hypothesized causal model, and showing non-null effect in a predicted range, are two possible solutions.

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  19. If you perform a null-hypothesis test you need to justify why a null-effect is plausible. Random assignment to conditions is one justification. Without random assignment, the null is often not plausible. It is not surprising to reject null effects when comparing existing groups.

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  20. Retweeted
    Jan 29

    Hooray! 5 years' work, some great reviewers and 'Best practice guidance for linear mixed-effects models in psychological science' is in press at Journal of Memory and Language. 50 free copies: TLDR: FOR GOODNESS SAKE REPORT MODELS & RESULTS IN FULL. 1/

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  21. Retweeted
    Jan 29

    Is the WEIRD acronym falsifiable? Good question! [Thread]

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