kxxsc

You can trade Coinbase shared pre-IPO on FTX (unfortunately, not available to US users). The stock trading infra seems weird but makes sense under the hood and is reliable (happy to answer any questions people have).

https://ftx.com/trade/CBSE/USD

(this was mostly informational, but if anyone actually decides to use the platform and wants to get 5% off trading fees, also happy to share a referral link)

I think you are underestimating the degree to which requires only a phone number is a killer feature:

-you download the app and can immediately get started after verifying your phone number (no ID or password required) -you don't need to share any ID or connection details other than just your phone number

I spend a lot of time in India, and I think this lack of complexity has contributed significantly to its virality (I'd estimate that a pretty significant percentage of the user base does not have or regularly use an email account, which is usually a prerequisite to setting up many accounts).

The science of swing bowling is fascinating, and makes cricket watching a beautiful, nuanced affair.

This article helped me understand the basics of swing, and explains why moisture in the air makes a huge difference, as does the condition of the ball: https://www.espncricinfo.com/story/_/id/17104279/the-science...

One of the most successful fast bowlers in cricket, James Anderson, is notorious for his ability to be able to swing the ball when there is moisture in the air. So much so, that he is referred to as James "Clouderson": https://www.quora.com/Why-is-James-Anderson-known-as-Clouder...

I think there might be an issue with your FAQs section. I couldn't see the answers and it redirects me to "https://www.masterywithsql.com/#", taking me back to the top of the page.

The course looks very cool by the way.

This comment is surprising to me. Most data scientists use results and measures of statistical significance provided by the program they're using, which accounts for the distribution used. Do you have examples where either data scientists are not presenting aggregate statistics or where someone is using the wrong kinds of p-values?

To your specific examples - the coefficient of a linear regression is distributed normally (?). Similarly, we know the expected distribution of most maximum likelihood estimators (logistic regression, etc.), and programs will give you the right p-value.

Of course, omitted variable bias is still a problem and it is possible to mis-specify your model. However, I think most data scientists are presenting aggregate statistics (means, regression coefficients) like you said, and that we have a pretty good handle on the underlying distributions.

The table with the breakdown of a typical premium really tells a story. No better evidence of America's broken and bloated healthcare-government-insurance complex than the fact that only 46% of the premium is actually used for claims.

If you're interested, the National Bureau of Economic Research has some pretty interesting work on the subject:

Stock Price Reactions to Index Inclusion: http://www.nber.org/digest/nov13/w19290.html

The Mysterious Growing Value of S&P 500 Membership: http://www.nber.org/papers/w8654

Big Picture: Inclusion in an index does have some +ve effect on price