Statistical Inference

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.

> Não sei qual a demência de existir uma lei (da qual eu acho que foi derrubada) proibindo sobre

A explicação pra isso é baseada no teorema de Bayes. Essa aula de 7 minutos dá uma visão mais clara sobre o assunto [1]. Basicamente, todo exame tem associado duas informações: a sensibilidade e a especificidade [2]. Basicamente, o que o pessoal faz primeiro é dividir a população em grupos (grupos de risco e grupos sem ser de risco para a doença em análise). Depois, usam essas informações juntamente com a especificidade e a sensibilidade do exame e aplicam o teorema de Bayes pra calcular a taxa de falso positivo e falso negativo do exame, dado o grupo que você se encontra. Dependendo do caso em questão, a porcentagem de falso negativo pode ser muito grande.

O link em [1], que é um vídeo de 7 minutos, mostra um exemplo pra Aids. Não sei se os dados são só ilustrativos ou se são reais, mas o mais importante nele é explicar o conceito.

Antes de entender essa lógica eu imaginava que era uma questão puramente preconceituosa. Hoje já vejo que tem uma questão científica por trás. E aí fico pensando o que poderia ser feito pra resolver isso. Cheguei nas seguintes possibilidades:

• Essa política era bem antiga. Será que a mesma política faz sentido considerando exames mais modernos?

• Será que a política atual considera os mesmos dados de incidência de Aids na população homo/hetero que há 30 anos atrás? Ou considera dados atualizados? E com os dados atualizados, os resultados são os mesmos?

• Mesmo se os resultados dos itens anteriores forem os mesmos, isso significa que o resultado no grupo homossexuais tem resultado menos eficaz do que no grupo heterossexuais. Mas será que não daria para identificar o comportamento de risco no grupo e isolar? Em outras palavras, uma triagem exclusivamente baseada em comportamentos sexuais (múltiplos parceiros frequentes vs união estável etc) em vez de orientação sexual seria melhor? Ou pior?

Enfim, são perguntas que pra serem respondidas precisam de pesquisa. Muita pesquisa. Não é o nosso forte.

[1] https://www.coursera.org/learn/statistical-inference/lecture/dzuc6/03-02-bayes-rule

[2] https://www.fleury.com.br/noticias/precisao-diagnostica

Coursera has a class on statistical inference: https://www.coursera.org/learn/statistical-inference

Also, I highly recommend the book Doing Bayesian Analysis (it has four puppies on the cover).

In that case take a course on statistical Inference https://www.coursera.org/learn/statistical-inference and an introductory course on Database management https://www.coursera.org/learn/database-management after learning about SQL go to hackerank and solve their SQL problems to have a good grasp on effectively using SQL to fetch the data you want from database. Consider learning a programming language also python highly recommended.

I think that's a fair thing to learn - I would probably suggest breaking it down into smaller components instead of just taking an umbrella course like this (alternatively, if you have money and time to spare, this is probably going to be a solid intro - but I think you need to take it a few steps further.)

• Experimental/Study Design/Hypothesis testing

https://www.khanacademy.org/math/statistics-probability/designing-studies

• Statistical Sampling

Don't know a good moog for this one sorry, although I always recommend the GOAT statistical course/books:

Introduction to Statistical Learning/Elements of Statistical Learning

https://lagunita.stanford.edu/courses/HumanitiesSciences/StatLearning/Winter2016/about

• Statistical Inference

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

• Causal Inference

https://www.coursera.org/learn/crash-course-in-causality

I think a lot this is covered in the following specialization:

https://www.coursera.org/specializations/jhu-data-science?utm_medium=listingPage#courses

Yes, there are many free courses that you can use to learn the prerequisite mathematics. KhanAcademy would be my first recommendation, but you can also try some of these:

Inferential Statistics https://www.coursera.org/learn/inferential-statistics

Bayesian Statistics: From Concept to Data Analysis https://www.coursera.org/learn/bayesian-statistics

Inferential Statistics Intro https://www.coursera.org/learn/inferential-statistics-intro

Bayesian Statistics https://www.coursera.org/learn/bayesian

Basic Statistics https://www.coursera.org/learn/basic-statistics

Introduction to Probability https://www.edx.org/course/introduction-probability-science-mitx-6-041x-2

Introduction to Linear Models and Matrix Algebra https://www.edx.org/course/introduction-linear-models-matrix-harvardx-ph525-2x-2

Intro to Descriptive Statistics https://www.udacity.com/course/intro-to-descriptive-statistics--ud827

Intro to Inferential Statistics https://www.udacity.com/course/intro-to-inferential-statistics--ud201

Mathematics for Computer Science https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-042j-mathematics-for-computer-science-fall-2010/index.htm

An Intuitive Introduction to Probability https://www.coursera.org/learn/introductiontoprobability

Statistical Inference https://www.coursera.org/learn/statistical-inference

College Algebra and Problem Solving https://www.edx.org/course/college-algebra-problem-solving-asux-mat117x

Precalculus https://www.edx.org/course/precalculus-asux-mat170x

The bullshit formula you originally linked and scrubbed by editing. Along with that stack exchange. Cite some relevant sources as I did. You’re biased because you’re clueless about this and trying to defend some random douchebag on Twitter who is clearly not someone who understands statistical data collection, hypothesis, testing, inference and presentation. They mock people who actually do this work with that garbage in, garbage out method.

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