I am finishing the incredible helpful, free coursera course 'Improving your statistical inferences' - perfect to learn applied statistics

Heads up, Daniel Lakens launched his new course 'Improving Your Statistical Questions' on Coursera yesterday.

Don't be sleeping on Daniel Lakens. The new course is freely available (here)[https://www.coursera.org/learn/improving-statistical-questions]. Also check out his older course ('Improving Your Statistical Inferences')[https://www.coursera.org/learn/statistical-inferences?], can personally recommend that one. Bore your friends with your flawless interpretation of p-values and join the cool kids on (open) science twitter.

His blog, The 20% Statistician, has more specific topics such as (why you shouldn't calculate post hoc power)[http://daniellakens.blogspot.com/2014/12/observed-power-and-what-to-do-if-your.html], (why you should use Welch's instead of Student's t-test)[http://daniellakens.blogspot.com/2015/01/always-use-welchs-t-test-instead-of.html], (how to think straight about p-values)[http://daniellakens.blogspot.com/2019/09/improving-education-about-p-values.html] and other things you were always afraid to ask about stats.

Can't recommend this coursera class enough for exactly the situation you're in: https://www.coursera.org/learn/statistical-inferences

I sort of caught up with a mixture of some very good MOOCs:

• improving your statistical inferences by Eindhoven University of Technology is fantastic
• inferential statistics by University of Amsterdam is great
• statistical inference by John Hopkins is also great

And working through my own statistical problems by asking people on stats.stackexchange and other forums.

It sounds like this could help you: https://www.coursera.org/learn/statistical-inferences

I highly recommend Daniel Laken's "Improving your statistical inferences" course. It's free. It does not deal so much with specific tests, but the principles that underlie all of them. It goes through traditional frequentist approaches, through likelihood methods, to the now very modern Bayesian approaches. You listen to video lectures and do practice (using R) alongside. From time to time, lessons are separated by tests that show you how well you're doing.

If you want to have an intuitive understanding of stats, then I would go for it. I have learnt a lot from it, and I am by no means a beginner. I now recommend it to all my PhD students.

https://www.coursera.org/learn/statistical-inferences

100% this class IMO -- https://www.coursera.org/learn/statistical-inferences

Probably the best online stats class/stats class period I've taken, though it doesn't use MPlus.

Is it this course?

https://www.coursera.org/learn/statistical-inferences

https://www.coursera.org/learn/statistical-inferences/ would have helped me a lot when I started out. The homeworks are really straightforward so I don't think it will push your understanding as much as a full time course might, but I think the material is really well structured for achieving an understanding of how statistics work.

I don't think it goes into specific tests like Student's T-Test or ANOVA but I'm not done yet.

There's Daniel Lakens' Coursera class that does a good job going over the philosophical difference between the 2 schools of thoughts, but not much for the mathematical aspect.

https://www.coursera.org/learn/statistical-inferences

Fuck yeah:

These should be required for everyone (they're also easy):
Improving Your Statistical Inferences (Coursera)
Improving Your Statistical Questions (Coursera)

Then, you can get this book or download the PDF for free. There are also free videos that go with each chapter. This is like the bible of basic stats and will PROPERLY teach you general linear models (GLMs) and how to do them in R. You may have learned correlations, t-tests, and ANOVAs as if they were all different things: they're not. They're all GLMs and they're all fundamentally correlations.
That book has more advanced stuff after, but you don't necessarily need to learn it.

All that and you'll set for undergrad. If you do grad school, you'll learn that (just like any other time you learn math-related stuff), nobody uses that stuff you learned. Fret not, though: learning GLMs is still the foundation! The next step is to learn multilevel-modelling, which is a more advanced form of GLM that takes account of "nested" data. The classic example is students nested in classes nested in schools nested in school-districts: the fact that a bunch of data-points (students) all share a common classroom is ignored in basic GLMs but multilevel-modelling accounts for that. It's easy once you know how to do GLMs and it's just a different line of R code.

Also... learn R. You might start out uncomfortable with coding, but that's life: you gotta push through ignorance to learn. There are plenty of free introductory courses to learn R and this is one skill that will translate even if you pursue other things.

EDIT: If you bounce off the first book, check out Andy Field's "Discovering Statistics". It's the other main "bible" of stats praised by many. I've not used it since I went with the one I linked above (ISL).

Indeed. Lakens is generally pretty great. He's not a random psychologist in a position to make criticisms like these, he specifically focuses on methods and inference, and even has a course online (which I recommend):

https://www.coursera.org/learn/statistical-inferences

For an individual study, you wouldn't really care - all else being equal, the size of your effect must be larger for you to reject the null hypothesis with a smaller study.

However, due to things like publication bias, underpowered studies generally have smaller sample sizes, which means more studies can be run, which means that by chance a higher number of studies will have made a type I error. If you had all of the data available, this wouldn't be a problem, but because of the bias in what gets shared with others, this means that having a field with a lot of small sample sizes can result in high rates of publishing false-positives.

There is another problem you can encounter somewhat related to the situation you're describing. If you didn't quite get the N you wanted according to your power calculation, and you didn't quite get statistical significance, you might then decide to run a few more participants. This means you now have a flexible stopping rule, and that you are now capitalizing more on chance (as you would have stopped the study if you reached traditional significance). If you have access to peer-reviewed papers, you may want to check out False-Positive Psychology.

This is getting further afield from your question, but I'll also mention that traditional hypothesis testing only considers the strength of the evidence assuming the null hypothesis is true - it doesn't compare this to the strength of the evidence you'd expect if the alternative is true. This means that there are some cases where a traditionally significant p-value (.049) is actually more consistent with the null hypothesis than with a particular alternative hypothesis. For more information, Daniel Lakens has a great Coursera course - so even though the power might not matter if you achieved statistical significance, the strength of the evidence both against the null and for a particular alternative may matter.

This course is great: https://www.coursera.org/learn/statistical-inferences

I highly recommend this course: Improving your statistical inferences. I haven't done the last assignment but found the course very helpful in terms of thinking about statistics.

>11th out of 195 countries for highest death rate

There is your per capita statistic.

Lol I spent my summer doing an online Daniël Lakens learning how to do bayesian analysis and likelihood functions in R. But sure I don't understand statistics :) here it is if you're interested https://www.coursera.org/learn/statistical-inferences

I’m not aware of a single course/book that meets all your needs, but let me try to tackle them point by point.

For 1, 3, 5, and 7, I would strongly recommend “Improving your Statistical Inferences” by Daniel Lakens. It’s extremely accessible, present by video on Coursera, totally free, and covers lots of other really useful material such as likelihood-based inference, Bayes, effect sizes, and the importance of replication to testing a hypothesis. Very little math (some R), more conceptual but presented in an accessible way.

For 2, 3, and 6 - these seem to be more analysis-specific questions for which you might need a general textbook explaining different types of tests. Any intro stats textbook (the non-math heavy ones used in my field are typically “Discovering Statistics with SPSS/R” by Andy Field) will generally start to answer 2 and 6 in particular. Typically these types of books will give you some discrete type of decision-making chart (e.g. if this type of data, this type of experimental manipulation, then this test)

Let me give a little hint here though - I think it might be good to start learning generalised linear modelling strategies early, as these give you a lot of flexibility analysing almost anything. This is what Statistical Rethinking is really good for (among may other things), but there’s plenty of other resources available.

Although he and I have disagreements about statistical philosophy and practice, Daniel Lakens has a great MOOC for basic stats methods. https://www.coursera.org/learn/statistical-inferences

You might want to check out this course: https://www.coursera.org/learn/statistical-inferences/home/welcome

They explain sample size estimation and stuff like that.

But at the end of the day, only you decide how much participants will you collect. Underpowered studies with low sample are not useless, they can be included in a meta-analysis, for example.

Here you go! You might find his blog post interesting! Also Daniel made a MOOC. Both include R code that you can play around with. Let me know what you think.

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+ his blog post on observed power

https://www.coursera.org/learn/statistical-inferences/home/welcome

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I found the above course pretty useful.

Other commenters have mentioned learning statistics, and to that end I recommend this course:

https://www.coursera.org/learn/statistical-inferences

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But in general, understanding study design takes experience within a specific field.

If you want to read a paper like a scientist, the key is thinking like a scientist. A medical practitioner might read a clinical study and think "how can I apply this to my medical practise". A scientist on the other hand assumes the null hypothesis and only accept the finding if the methodology is of high quality, namely has managed to control all of the potential biases. Which is to say, you should always read a paper and consider all the ways it could be wrong - compare it to other papers in the field and related fields (often standards of evidence can vary dramatically between related fields, for example pharmacological vs nonpharmacological clinical trials). Consider all the possible sources of bias and look at how the authors have tried to control for it. Generally no study is perfect, so you will categorise papers into quality grades - for me it is "suggestive" quality evidence (interesting, but not generalisable), "moderate" quality evidence (generalisable but with significant caveats) and "high" quality evidence. In medical science, most studies fit into the "suggestive" and "moderate" categories. "High" quality evidence is reserved for findings that are found to be very robust in meta-analyses. In epidemiology, "high" quality evidence is that which is found in longitudinal population based studies (which helps minimise participation/selection/ecological biases) with high quality objective outcomes.

I've heard good things about Daniel Lakens' online course "Improving your statistical inferences". It's free to sign up:

https://www.coursera.org/learn/statistical-inferences

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It's more about understanding how we draw conclusions from our statistics than (e.g.) how to run a particular test, but I think that level of understanding is often missing from undergraduate courses.

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Same boat. I took that exact discrete mathematics class you are talking about. But yeah my stats is complete shit too.

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I just started this class and it has great reviews so hopefully its good: https://www.coursera.org/learn/statistical-inferences/home/welcome

I’m also a psych PhD always needing more stats training. Daniel Lakers has some nice courses on coursera (free, quick, easy). Probably plenty of things covered you already know but helps you consider more of the why and how questions you’re looking to understand.

https://www.coursera.org/learn/statistical-inferences

https://www.coursera.org/learn/statistical-inferences

You don't need to wait, necessarily. You can start with these two free online courses:
https://www.coursera.org/learn/statistical-inferences?
https://www.coursera.org/learn/improving-statistical-questions

My other thoughts are here. You'll get taught stats, but you'd do well to learn R in the meantime. There are free coursera courses and the Johns Hopkins Data Science one is good for the basics. That's what most students struggle with anyway.

Ultimately, the stats you learn are going to be extremely basic unless you take it upon yourself to learn more than the basic multilevel modelling everyone uses for regression. TBH you'll be way ahead if you understand even basic stats. Most psych professors cannot even correctly define what a p-value is so the competition isn't very stiff on that front.

I did some courses with this instructor, found her courses quite good.
https://www.coursera.org/instructor/minecetinkayarundel

just saw this course, haven't done it, keen to hear from others who have.
https://www.coursera.org/learn/statistical-inferences#instructors

I find I need to brush up regularly, bad habits creep in too easily.

The gold standard online course in this area is probably MIT's Statistics and Data Science MicroMasters. I went to a top university, and from my perspective, most online courses are somewhat dumbed-down compared to the brick-and-mortar versions, even when top universities are offering them. This MIT MicroMaster's is the only online course I've seen which isn't like that. I've only completed the first course so far, in probability, but it was hardcore. It's the only online course I ever saw where the forums are full of people complaining about how hard it is. It is also a steal relative to a real masters, at about $1200 for four online courses. If you know calculus and are comfortable with proofs, and you want to invest maybe a year or two part time, to gain an extremely good foundation in stats and machine learning and have something reasonably impressive you can put on your resume, this is what I would recommend.

This course was OK, but although it's not obvious from the description, it actually does assume a stats background I didn't have. Still got a decent amount out of it, but honestly a lot of the stuff in the course seems to me like patching up a fundamentally broken paradigm of null hypothesis testing. (On the other hand, if what you care about is evaluating someone else's data analysis, as opposed to doing your own, maybe that's what you want. In fact it might be the best course available for that specific purpose. This vid on p-curve analysis was pure gold. Even for people who know stats, I'd recommend watching it to learn about p-curve analysis; it's an extremely clever technique for figuring out if a collection of results are spurious during a meta-analysis.) This course was poorly explained in my opinion and turned me off of any more JHU data science courses. There are some other Coursera courses on stats which look like they could be good but I can't comment from personal experience.

I'd consider myself a student too :)

> So you’re saying people are ignoring or not appreciating how p values work, and how they account for sample size & observed variance?

Something like that. There's a ton written about this. And studies in some fields of misinterpretations of p-values and confidence intervals.

I think my views align closely with David Colquhoun's:

Pop science magazine: https://aeon.co/essays/it-s-time-for-science-to-abandon-the-term-statistically-significant

Peer-reviewed journal: Colquhoun, D. (2014). An investigation of the false discovery rate and the misinterpretation of p-values. Royal Society open science, 1(3), 140216.

If you're interested, you can look up his other articles.

I think Daniel Lakens also speaks pretty clearly about this stuff (although sometimes I think he should just go Bayesian) and from a fairly practical perspective (he's a practicing psychologist). He has a well-received coursera and some good blog posts.

Also relevant: Button, K. S., Ioannidis, J. P., Mokrysz, C., Nosek, B. A., Flint, J., Robinson, E. S., & Munafò, M. R. (2013). Power failure: why small sample size undermines the reliability of neuroscience. Nature reviews neuroscience, 14(5), 365-376.