You gotta think about this one carefully!

Imagine you go to the doctor and get tested for a rare disease (only 1 in 10,000 people get it.)

The test is 99% effective in detecting both sick and healthy people.

Your test comes back positive.

Are you really sick? Explain below 👇

The most complete answer from every reply so far is from Dr. Lena. Thanks for taking the time and going through it!

https://t.co/jGt006Vlh5
You can get the answer using Bayes' theorem, but let's try to come up with it in a different —maybe more intuitive— way.

👇
Here is what we know:

- Out of 10,000 people, 1 is sick
- Out of 100 sick people, 99 test positive
- Out of 100 healthy people, 99 test negative

Assuming 1 million people take the test (including you):

- 100 of them are sick
- 999,900 of them are healthy

👇
Let's now test both groups, starting with the 100 people sick:

▫️ 99 of them will be diagnosed (correctly) as sick (99%)

▫️ 1 of them is going to be diagnosed (incorrectly) as healthy (1%)

👇
Let's now test the group of 999,900 healthy individuals:

▫️ 989,901 of them will be diagnosed (correctly) as healthy (99%)

▫️ 9,999 of them will be diagnosed (incorrectly) as sick (1%)

👇
Since your test came back positive, it means that you belong to either one of the groups that had a positive result:

1. 99 people that are truly sick, or
2. 9,999 people that are actually healthy (but were diagnosed as sick.)

👇
Basically, out of 10,098, only 99 are truly sick.

That'll give you a 0.98% chance of being sick!

So no, most likely, you are fine!

👇
Here is something important: this is true as long as our only priors are that 1 in 10,000 people have the disease.

For example, if you were showing symptoms, then your chance of being sick after receiving a positive test will be higher.

More from Santiago

Free machine learning education.

Many top universities are making their Machine Learning and Deep Learning programs publicly available. All of this information is now online and free for everyone!

Here are 6 of these programs. Pick one and get started!



Introduction to Deep Learning
MIT Course 6.S191
Alexander Amini and Ava Soleimany

Introductory course on deep learning methods and practical experience using TensorFlow. Covers applications to computer vision, natural language processing, and more.

https://t.co/Uxx97WPCfR


Deep Learning
NYU DS-GA 1008
Yann LeCun and Alfredo Canziani

This course covers the latest techniques in deep learning and representation learning with applications to computer vision, natural language understanding, and speech recognition.

https://t.co/cKzpDOBVl1


Designing, Visualizing, and Understanding Deep Neural Networks
UC Berkeley CS L182
John Canny

A theoretical course focusing on design principles and best practices to design deep neural networks.

https://t.co/1TFUAIrAKb


Applied Machine Learning
Cornell Tech CS 5787
Volodymyr Kuleshov

A machine learning introductory course that starts from the very basics, covering all of the most important machine learning algorithms and how to apply them in practice.

https://t.co/hD5no8Pdfa

More from Health

Before we get too far into 2021, I thought I’d write a thread recapping some of the research that came out of my lab in 2020. Most of this work was led by my talented team of graduate students, Kerrianne Morrison, @kmdebrabander, and @DesiRJones.

Back in January, a news story was published about Kerrianne’s study showing improved social interaction outcomes for autistic adults when paired with another autistic partner.

A detailed thread about the study and a link to the paper can be found here (feel free to DM me your email address if you’d like a copy of the full paper for this study or any of our studies):


Another paper published early in 2020 (it appeared a few months earlier online) showed that traditional standalone tasks of social cognition are less predictive of functional and social skills among autistic adults than commonly assumed in autism research.


Next, @kmdebrabander led and published an innovative study about how well autistic and non-autistic adults can predict their own cognitive and social cognitive performance.
Thread on how atheism leads to mental retardation (backed with medical citations🧵💉)

To start with, atheism is an unnatural self-contradicting doctrine.

Medical terminology proves that human beings are naturally pre-disposed to believe in God. Oxford scientists assert that people are "born believers".

https://t.co/kE0Fi588yn
https://t.co/OqyXcGIMJn


It should be known that atheism could never produce an intelligently-functioning society and neither ever will.

Contrastingly, Islam produced several intellectuals & polymaths, was on the forefront of scientific development, boasting 100% literacy


It is also scientifically proven that atheism led to lesser scientific curiosity and scientific frauds, which is also why atheists incline to pseudo-science.

Whereas, religion in general and Islam in particular boosted education.

https://t.co/19Onc84u3g


Atheists are also likely to affected by pervasive mental and developmental disorders like high-functioning autism.

Cognitive Scientists and renowned Neurologists found that more atheism is leads to greater autism.

https://t.co/zRjEyFoX3P

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