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Got an email from a new grad student asking for recommendations for resources to better understand research design and statistical inference. Here’s what I’m going to tell them...
My first recommendation on learning these concepts is to begin by improving *how* you learn and work. I use the pomodoro technique, which I outline in this thread
I’m not impressed when someone tells me they work 60+ hours a week—anyone can sit in front of a computer for 60 hours a week but what matters is what you *do* when you’re in front of your computer
A few years ago I read Cal Newport’s “Deep Work” and it changed the way I work. Sure, there’s a little bit of dicey pop psych in the book but this doesn’t detract from the overall message and I highly recommend it amazon.com/Deep-Work-Focu… 
Another book I recommend is “Measure what Matters”, which covers how to set objectives and measure your results. These principles work for both a group level and individual level amazon.com/Measure-What-M… Also provides a framework on what opportunities to say “no” to...
If you’re interested in a career in academia, I would put the time in to learn #Rstats. When it comes to applying statistical inference in R, check out @djnavarro’s excellent book learningstatisticswithr.com
When it comes data wrangling in #Rstats, check out these fantastic data skills resources from @dalejbarr and @LisaDeBruine gupsych.github.io/data_skills/
Data simulation is an under-appreciated tool, which can really help with study design. Here’s a handy paper from Lisa and Dale psyarxiv.com/xp5cy/
If you’re not planning a long-term career in academia and you only anticipate performing common statistical tests, then @jamovistats will do the trick. Here’s a JAMOVI adaption of Danielle’s book I mentioned above from @davidfoxcroft sites.google.com/brookes.ac.uk/…
#Rstats is not for everyone, so don’t learn it unless you have to. Same goes for any other trendy tool.
Don’t be an academic hipster everythinghertz.com/39
Don’t be an academic hipster everythinghertz.com/39
Two MOOCs that I recommend:
1. Improving your statistical inferences from @lakens coursera.org/learn/statisti…
2. Open science MOOC from @Protohedgehog + team opensciencemooc.eu
1. Improving your statistical inferences from @lakens coursera.org/learn/statisti…
2. Open science MOOC from @Protohedgehog + team opensciencemooc.eu
This post from @richarddmorey probably the best piece on statistical power that I’ve read towardsdatascience.com/why-you-should… check out his associated JAMOVI module
If you’re interested in Bayesian hypothesis testing, check out this paper from @EJWagenmakers + team link.springer.com/article/10.375… for a briefer read, check out our paper on the same topic bmcpsychiatry.biomedcentral.com/articles/10.11…
I came across almost all of these resources on Twitter, so it’s worth getting involved with the platform to keep up-to-date. I might still be waiting for SPSS to load if it wasn’t for Twitter 😉
If there are any other resources that you’ve found handy, let me know
If there are any other resources that you’ve found handy, let me know
Speaking of pomodoros, we just released a new @hertzpodcast episode, in which we (try to) apply strict time limits to our discussion topics
