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# ##  Statistical analysis
# * Learning statistics or want to brush up? Here are three textbooks (available online) that you can choose from depending on the depth you want to explore and mathematical background.
#   * [Introduction to Statistics](http://onlinestatbook.com/Online_Statistics_Education.pdf) -- cover the fundamentals, requires little mathematical background. Another great introduction is Statistics Without Tears by Rowntree.
#   * [All of Statistics](https://www.ic.unicamp.br/~wainer/cursos/1s2013/ml/livro.pdf) -- more detailed than above, requires calculus and linear algebra
#   * [Advanced data analysis](http://www.stat.cmu.edu/~cshalizi/ADAfaEPoV/ADAfaEPoV.pdf) -- if you dream about distributions, requires substantial statistical background
# * If you are teaching statistics, [here](http://students.brown.edu/seeing-theory/) are excellent visualizations of core concepts.
#
# * See this [tutorial](http://m-clark.github.io/documents/) on machine learning concepts.
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# * Learn to love Bayesian statistics, if you don't already. Read [this](http://jakevdp.github.io/blog/2014/03/11/frequentism-and-bayesianism-a-practical-intro/) introduction on bayesian vs. frequentist statistics written by [<NAME>](https://staff.washington.edu/jakevdp/), an astrophysicist and python developer.
#
# * Looking for a Bayesian analysis package? Try [JASP](https://jasp-stats.org/).
#
# * Beware of p-values and null hypothesis significance testing (NHST) the de facto standard in neurobiology, cognitive neuroscience and much of biomedical research:
#   * [Beyond the dead salmon](https://slides.com/neuro_logical/pvps): talk on p-values and how it relates to the current reproducibility crisis and pragmatic approaches to the problem.
#   * <NAME> on how not to get fooled by p-values: [video](https://videocast.nih.gov/summary.asp?Live=26200&bhcp=1Read)
#   * Problems with p-values: [1](http://ejwagenmakers.com/2007/pValueProblems.pdf) [2](https://peerj.com/articles/3544.pdf)
#   * How to proceed: [Abandon statistical thresholds](http://www.stat.columbia.edu/~gelman/research/unpublished/abandon.pdf)
# * Do you have multi-level data? E.g. do you have some cells from one animal and some other cells from a different animal? Are you pooling the data because they have similar distributions/variance? Instead you might want to consider [hierarchical](https://en.wikipedia.org/wiki/Multilevel_model) aka mixed effect models. [Here](http://mfviz.com/hierarchical-models/) is a really beautiful demonstration of this concept.
# * As soon as possible, understand:
#   * [bootstrapping](https://en.wikipedia.org/wiki/Bootstrapping_(statistics)) -- insanely powerful.
#   * [cross-validation](https://en.wikipedia.org/wiki/Cross-validation_(statistics)) -- enables generalization
#   * [permutation tests](https://en.wikipedia.org/wiki/Resampling_(statistics)) -- gets you a null distribution, sometimes hard to analytically derive in closed form
#   * [Here](https://docs.google.com/presentation/d/11TozBxAaON1eFXeL6aK1USLtJyAbUaHhskcPkI0FLbc/edit#slide=id.g138cbbed1a_0_0 ) are some lecture notes that look at these topics in the context of multivariate pattern analysis in fMRI.
#
# *	Do not let your test data into your training data (i.e. double dipping)
#
# * [<NAME>](http://www.stat.cmu.edu/~kass/), @CMU statistics, has written the extremely useful [Ten Simple Rules for Effective Statistical Practice](http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1004961).
#
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# * Understand the bias/variance [trade off](https://ml.berkeley.edu/blog/2017/07/13/tutorial-4/)
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# * Do you know what the chris rock effect is in statistics? Expand your statistical lexicon [here](http://andrewgelman.com/2009/05/24/handy_statistic/).
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# * Are you still not using hierarchical Bayes? <NAME> will show you the [way](http://twiecki.github.io/blog/2017/02/08/bayesian-hierchical-non-centered/). [Everything is a trivial case of hierarchical Bayesian inference](https://twitter.com/farlkriston/status/699515098050723840).
#
# * Frequent [datatau](http://www.datatau.com/) for interesting news on data analysis.
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# * Your stats question may have an answer over [here](https://stats.stackexchange.com/) on cross-validated.
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#
#
#   ###  Statistics blogs
#   * [While my MCMC gently samples](http://twiecki.github.io/)
#
#   * [Pythonic perambulations](http://jakevdp.github.io/)
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#   * [Statistical Modeling, Causal Inference, and Social Science](http://andrewgelman.com/)
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#   * Wagenmakers new blog! [Bayesian Spectacles](https://www.bayesianspectacles.org)

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