Statistical Musings

No technical details in this post. Just a few scattered thoughts and some stories that have kept me semi-entertained over the last month. Some are inspired by work and others are just my take on the world, exaggerated to some degree.

Rademacher Coins

As we move towards a cashless society, maybe it would make sense to do away with coins and decimal values. Decimal points make billed amount and account balances unnecessarily messy and untidy. Removing the decimals is a simple issue. After all, the current accounting system rounds to 2 decimal place and it would not be hard to just round it to the nearest integer. I guess the main concern is fairness. For billed amounts where the value is low, rounding up or down will translate to a significant percentage change in the price of a good.

Here's my suggestion to the problem: randomly round up or down all transactions to the nearest dollar. On average, it would translate to paying / receiving 50 cents and assuming that no one would game the system (e.g. by trying to make sure that the total bill ends up greater than 50 cents per transaction), the system would be fair and everyone gets the benefit of round numbers.

All transactions would be validated by a blockchain system and early adopters would be rewarded with Rademacher coins. As given away by its name, the coins will have a value of either -1 or 1 with 50% probability.

Data Science

Of all job descriptions with the title of data science, 80% involves data while only 20% have any relation to science. By science, I do not mean related to fields such as Chemistry, Biology or Physics, but rather the scientific method of generating hypothesis, experimenting and falsifying claims.

Maybe that figure is too low. A more accurate survey would show that 50% of data scientists are actually conducting experiments and analysis to confirm the prior expectations of their bosses. After all, data is truth.

Algorithmic World

Financial transactions can be conducted with a wave of a phone. Cars ply the road with no drivers in sight. Lampposts act as a weather vane, traffic monitoring device and surveillance tool.

Corporations still exist, though they are now claim to provide work-life balance and free pantry snacks compared to those a decade ago. The capitalist points to this as a working of competitive markets and the benefit of labour mobility and international trade. The neo-neo-marxist claims that men are still tied in chains (or rather stimulated by syntactic sugar) while the capitalist (to be precise they are now called venture capitalist) reaps the bitcoin.

In one of these container blocks a data scientist presents his new predictive model, capable of profiling an individual to a level and detail never yet seen before.

Customer i have a 70% probability of purchasing an iPhone X, 36% probability of contracting cancer by the age of 60 and 0.01% chance of getting eaten by a shark.

The audience was intrigued. A flurry of questions were raised:

What model did you use?
What is the underlying prevalence rate?
What is the precision score and AUC?
How do we automate the process and scale it up?
How do we ensure secure authentication and information storage?
What is the expected profits?
When can we use the model?

But, no one asks why?