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Statistics

A collection of 116 issues

No Stopping: Impact of the Stopping Rule on the Sex Ratio

For social scientists brought up to worry about bias stemming from stopping data collection when results look significant, the fact that a gender-based stopping rule has no impact on the sex ratio seems suspect. So let’s dig deeper. Let there be n families, and let the stopping rule be

ML (O)Ops: What Data To Collect? (part 3)

The first part of the series, “Improving and Deploying On-Device Models With Confidence,” is posted here. The second part, “Keeping Track of Changes,” is posted here. With Atul Dhingra For a broad class of machine learning problems, nitpicking over the neural net architecture is over (see, for instance, here). Instead,

The True Ones: Best Guess of True Proportion of 1s

ML models are generally used to make predictions about individual observations. Sometimes, however, the business decision is based on aggregate data. For example, say a company sells pants and wants to know how many will be returned over a certain period. Say the company has an ML model that predicts

ML (O)Ops! Keeping Track of Changes (Part 2)

The first part of the series, “Improving and Deploying On-Device Models With Confidence”, is posted here. With Atul Dhingra One way to automate classification is to compare new instances to a known list and plug in the majority class of the exact match. For such instance-based learning, you often don’

ML (O)Ops! Improving and Deploying On-Device Models With Confidence (Part 1)

With Atul Dhingra. Part 1 of a multi-part series. It is well known that ML Engineers today spend most of their time doing things that do not have a lot to do with machine learning. They spend time working on technically unsophisticated but important things like deployment of models, keeping

Build Software for the Lay User

Most word processing software helpfully point out grammatical errors and spelling mistakes. Some even autocorrect. And some, like Grammarly, even give style advice. Now, consider software used for business statistics. Say you want to compute the correlation between two vectors: [100, 2000, 300, 400, 500, 600] and [1, 2, 3,

Superhuman: Can ML Beat Human-Level Performance in Supervised Models?

A supervised model cannot do better than its labels. (I revisit this point later.) So the trick is to make labels as good as you can. The errors in labels stem from four sources: 1. Lack of Effort: The more effort people spend labeling something, presumably, the more accurate it

Too Much Churn: Estimating Customer Churn

A new paper uses financial transaction data to estimate customer churn in consumer-facing companies. The paper defines churn as follows: There are three concerns with the definition: 1. The definition doesn’t make clear what the normalizing constant is for calculating the share. Given that the value “can vary between

94.5% Certain That Covid Vaccine Will Be Less Than 94.5% Effective

“On Sunday, an independent monitoring board broke the code to examine 95 infections that were recorded starting two weeks after volunteers’ second dose — and discovered all but five illnesses occurred in participants who got the placebo.”Moderna Says Its COVID-19 Vaccine Is 94.5% Effective In Early Tests The data

Fat Or Not: Toward 'Proper Training of DL Models'

A new paper introduces a DL model to enable ‘computer aided diagnosis of obesity.’ Some concerns: 1. Better baselines: BMI is easy to calculate and it would be useful to compare the results to BMI. 2. Incorrect statement: The authors write: “the data partition in all the image sets are