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Confabulation in the humanities - Matthew Lincoln, PhD
Now, realize that this doesn't _just_ apply to interpreting quantitative analyses, but also to more traditionally-humanistic explanations...
data_analysis  humanities  everything_is_obvious_once_you_know_the_answer  to_teach  via:?  have_read 
7 days ago by cshalizi
Supercentenarians and the oldest-old are concentrated into regions with no birth certificates and short lifespans | bioRxiv
"The observation of individuals attaining remarkable ages, and their concentration into geographic sub-regions or ‘blue zones’, has generated considerable scientific interest. Proposed drivers of remarkable longevity include high vegetable intake, strong social connections, and genetic markers. Here, we reveal new predictors of remarkable longevity and ‘supercentenarian’ status. In the United States, supercentenarian status is predicted by the absence of vital registration. The state-specific introduction of birth certificates is associated with a 69-82% fall in the number of supercentenarian records. In Italy, which has more uniform vital registration, remarkable longevity is instead predicted by low per capita incomes and a short life expectancy. Finally, the designated ‘blue zones’ of Sardinia, Okinawa, and Ikaria corresponded to regions with low incomes, low literacy, high crime rate and short life expectancy relative to their national average. As such, relative poverty and short lifespan constitute unexpected predictors of centenarian and supercentenarian status, and support a primary role of fraud and error in generating remarkable human age records."

--- This is a lovely little case study.
to:NB  have_read  data_collection  demography  bureaucracy  statistics  to_teach  via:kjhealy  fraud 
15 days ago by cshalizi
Data visualization: Data visualization depicts information in graphical form.
"Principles:
Data visualization is a form of communication that portrays dense and complex information in graphical form. The resulting visuals are designed to make it easy to compare data and use it to tell a story – both of which can help users in decision making.

Data visualization can express data of varying types and sizes: from a few data points to large multivariate datasets."
teaching:data  teaching:graphing  graphics  visualization  to_teach  statistics:visualization 
7 weeks ago by hallucigenia
Confidence intervals: not a very strong property - Biased and Inefficient
Cute. (The "Gygax intervals" in paragraph 2 are what I use in teaching to say that coverage, while essential, isn't _enough_.)
statistics  confidence_sets  lumley.thomas  to_teach 
9 weeks ago by cshalizi
In 2017, the feds said Tesla Autopilot cut crashes 40%—that was bogus | Ars Technica
Unfortunately, the mistake here is so bald that it'd be hard to turn into a good teaching example.
bad_data_analysis  to_teach  driverless_cars 
february 2019 by cshalizi
Polar Vortex 2019: Why Forecasts Are So Accurate Now - The Atlantic
Actually teaching this would mean learning a lot about the history & current state of weather forecasting...
prediction  meteorology  to_teach 
february 2019 by cshalizi
Bayesian Surprise — the Shiny app
I wrote a while back about a toy case of the Bayesian surprise problem: what does Bayes Theorem tell you to believe when you get really surprising data. The one-dimensional case is a nice math-stat problem, if you like that sort of thing, but maybe you’d rather have the calculations done for you.

Here’s an app
statistics:bayesian  statistics:heavy_tailed_distributions  to_teach  R  shiny 
february 2019 by hallucigenia
Spatio-Temporal Statistics with R
The world is becoming increasingly complex, with larger quantities of data available to be analyzed. It so happens that much of these “big data” that are available are spatio-temporal in nature, meaning that they can be indexed by their spatial locations and time stamps.

Spatio-Temporal Statistics with R provides an accessible introduction to statistical analysis of spatio-temporal data, with hands-on applications of the statistical methods using R Labs found at the end of each chapter. The book:

* Gives a step-by-step approach to analyzing spatio-temporal data, starting with visualization, then statistical modelling, with an emphasis on hierarchical statistical models and basis function expansions, and finishing with model evaluation
* Provides a gradual entry to the methodological aspects of spatio-temporal statistics
* Provides broad coverage of using R as well as “R Tips” throughout.
* Features detailed examples and applications in end-of-chapter Labs
* Features “Technical Notes” throughout to provide additional technical detail where relevant
* Supplemented by a website featuring the associated R package, data, reviews, errata, a discussion forum, and more

The book fills a void in the literature and available software, providing a bridge for students and researchers alike who wish to learn the basics of spatio-temporal statistics. It is written in an informal style and functions as a down-to-earth introduction to the subject. Any reader familiar with calculus-based probability and statistics, and who is comfortable with basic matrix-algebra representations of statistical models, would find this book easy to follow. The goal is to give as many people as possible the tools and confidence to analyze spatio-temporal data.
to_read  to_teach  spatial_ecology  statistics:spatial  statistics:time_series  tutorial  R 
january 2019 by hallucigenia
PsyArXiv Preprints | Do smartphone usage scales predict behaviour?
"Understanding how people use technology remains important, particularly when measuring the impact this might have on individuals and society. However, despite recent methodological advances in portable computing and the ability to record digital traces of behaviour, research concerning smartphone use overwhelmingly relies on self-reported assessments, which have yet to convincingly demonstrate an ability to predict objective behaviour. Here, and for the first time, we compare a variety of smartphone use and ‘addiction’ scales with objective behaviours derived from Apple’s Screen Time application. While correlations between psychometric scales and objective behaviour are generally poor, measures that attempt to frame technology use as habitual rather than ‘addictive’ correlate more favourably with subsequent behaviour. We conclude that existing self-report instruments are unlikely to be sensitive enough to accurately predict basic technology use related behaviours. As a result, conclusions regarding the psychological impact of technology are unreliable when relying solely on these measures to quantify typical usage."

--- Tagged "to teach" because this is a great example of the actual foundations of statistics (namely, knowing where the numbers came from and what they mean), but I don't know what class I'd teach this in.
to:NB  to_read  networked_life  social_measurement  psychometrics  to_teach 
december 2018 by cshalizi
Inferential Statistics is not Inferential – sci five | University of Basel – Medium
"The earth is flat (p > 0.05).

I confess. Throughout my scientific life, I have used a method that I knew or felt was deeply flawed. What’s more, I admit to have taught — and I do still teach — this method to my students. I have a number of questionable excuses for that. For example, because the method has shaped a big part of science in the last century, I think students ought to know about it.

But I have increasingly come to believe that science was and is largely a story of success in spite of, and not because of, the use of this method. The method is called inferential statistics. Or more precisely, hypothesis testing.
The method I consider flawed and deleterious involves taking sample data, then applying some mathematical procedure, and taking the result of that procedure as showing whether or not a hypothesis about a larger population is correct."
statistics:bayesian  statistics:frequentist  confidence_intervals  to_teach  regression  statistics  philosophy_of_science  philosophy_of_statistics  statistics:error_based 
december 2018 by hallucigenia
Object-oriented Computation of Sandwich Estimators | Zeileis | Journal of Statistical Software
"Sandwich covariance matrix estimators are a popular tool in applied regression modeling for performing inference that is robust to certain types of model misspecification. Suitable implementations are available in the R system for statistical computing for certain model fitting functions only (in particular lm()), but not for other standard regression functions, such as glm(), nls(), or survreg(). Therefore, conceptual tools and their translation to computational tools in the package sandwich are discussed, enabling the computation of sandwich estimators in general parametric models. Object orientation can be achieved by providing a few extractor functions' most importantly for the empirical estimating functions' from which various types of sandwich estimators can be computed."
to:NB  computational_statistics  R  estimation  regression  statistics  to_teach 
october 2018 by cshalizi
5601 Notes: The Sandwich Estimator
I believe the subscript in n inside the sums defining V_n and J_n should be i. Otherwise, this is terrific (unsurprisingly).
to:NB  to_teach  have_read  statistics  estimation  fisher_information  misspecification  geyer.charles 
october 2018 by cshalizi
precisely: An R package for estimating sample size based on precision
"precisely is a study planning tool to calculate sample size based on precision rather than power. Power calculations are focused on whether or not an estimate will be statistically significant; calculations of precision are based on the same prinicples as power calculation but turn the focus to the width of the confidence interval.

This package includes a Shiny app to help with calculations, which you can start with launch_precisely_app(). You can also find a live version at malcolmbarrett.shinyapps.io/precisely."
shiny  R_packages  statistics:experimental_design  experimnents  to_try  to_teach 
october 2018 by hallucigenia
How People Learn II Learners, Contexts, and Cultures
The second report from the National Academies of Science Engineering and Medicine, sumamarizing research into how people learn. Definitely going to be handy going ahead...
learning  pedagody  psychology  psychology:applied  to_teach  to_read  Books 
october 2018 by hallucigenia
Making it easier to discover datasets
Data set search; not sure how well it really works yet (or how long it will live, before Google breaks it. [Why, yes, I am still bitter about Reader.])
data_sets  to_teach 
september 2018 by cshalizi

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